
import refinitiv.data as rd  # This is LSEG's Data and Analytics' API wrapper, called the Refinitiv Data Library for Python.
from refinitiv.data.content import historical_pricing  # We will use this Python Class in `rd` to show the Implied Volatility data already available before our work.
from refinitiv.data.content import search  # We will use this Python Class in `rd` to fid the instrument we are after, closest to At The Money.
import refinitiv.data.content.ipa.financial_contracts as rdf  # We're going to need thtis to use the content layer of the RD library and the calculators of greeks and Impl Volat in Instrument Pricing Analytics (IPA) and Exchange Traded Instruments (ETI)
from refinitiv.data.content.ipa.financial_contracts import option  # We're going to need thtis to use the content layer of the RD library and the calculators of greeks and Impl Volat in IPA & ETI

import numpy as np  # We need `numpy` for mathematical and array manipilations.
import pandas as pd  # We need `pandas` for datafame and array manipilations.
import calendar  # We use `calendar` to identify holidays and maturity dates of intruments of interest.
import pytz  # We use `pytz` to manipulate time values aiding `calendar` library. to import its types, you might need to run `!python3 -m pip install types-pytz`
import pandas_market_calendars as mcal  # Used to identify holidays. See `https://github.com/rsheftel/pandas_market_calendars/blob/master/examples/usage.ipynb` for info on this market calendar library
from datetime import datetime, timedelta, timezone  # We use these to manipulate time values
from dateutil.relativedelta import relativedelta  # We use `relativedelta` to manipulate time values aiding `calendar` library.
import requests  # We'll need this to send requests to servers vie a the delivery layer - more on that below

# `plotly` is a library used to render interactive graphs:
import plotly.graph_objects as go
import plotly.express as px  # This is just to see the implied vol graph when that field is available
import matplotlib.pyplot as plt  # We use `matplotlib` to just in case users do not have an environment suited to `plotly`.
from IPython.display import clear_output, display  # We use `clear_output` for users who wish to loop graph production on a regular basis. We'll use this to `display` data (e.g.: pandas data-frames).
from plotly import subplots
import plotly

# Let's authenticate ourseves to LSEG's Data and Analytics service, Refinitiv:
try:  # The following libraries are not available in Codebook, thus this try loop
    rd.open_session(config_name="C:\\Example.DataLibrary.Python-main\\Example.DataLibrary.Python-main\\Configuration\\refinitiv-data.config.json")
    rd.open_session("desktop.workspace")
except:
    rd.open_session()


print(f"Here we are using the refinitiv Data Library version {rd.__version__}")

Here we are using the refinitiv Data Library version 1.0.0b20


!python -V

Python 3.8.12


response1 = search.Definition(
    view = search.Views.SEARCH_ALL, # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`
    query=".STOXX50E",
    select="DocumentTitle, RIC, StrikePrice, ExchangeCode, ExpiryDate, UnderlyingAsset, " +
            "UnderlyingAssetName, UnderlyingAssetRIC, ESMAUnderlyingIndexCode, RCSUnderlyingMarket" +
            "UnderlyingQuoteName, UnderlyingQuoteRIC",
    filter="RCSAssetCategoryLeaf eq 'Option' and RIC eq 'STX*' and DocumentTitle ne '*Weekly*'  " +
    "and CallPutOption eq 'Call' and ExchangeCode eq 'EUX' and " +
    "ExpiryDate ge 2022-07-10 and ExpiryDate lt 2023-07-22",  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.
    top=100,
).get_data()
searchDf1 = response1.data.df


searchDf1


currentUnderlyingPrc = rd.get_history(
    universe=[searchDf1.UnderlyingQuoteRIC[0][0]],
    fields=["TRDPRC_1"],
    interval="tick").iloc[-1][0]


currentUnderlyingPrc

4281.93


searchDf1.iloc[(searchDf1['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]]


def Get_exp_dates(year, days=True, mcal_get_calendar='EUREX'):
    '''
    Get_exp_dates Version 2.0:

    This function gets expiration dates for a year for NDX options, which are the 3rd Fridays of each month.

    Changes
    ----------------------------------------------
    Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code to allow
        (i) for the function's holiday argument to be changed, and defaulted to 'EUREX' as opposed to 'CBOE_Index_Options' and
        (ii) for the function to output full date objects as opposed to just days of the month if agument days=True.

    Dependencies
    ----------------------------------------------
    Python library 'pandas_market_calendars' version 3.2

    Parameters
    -----------------------------------------------
    Input:
        year(int): year for which expiration days are requested

        mcal_get_calendar(str): String of the calendar for which holidays have to be taken into account. More on this calendar (link to Github chacked 2022-10-11): https://github.com/rsheftel/pandas_market_calendars/blob/177e7922c7df5ad249b0d066b5c9e730a3ee8596/pandas_market_calendars/exchange_calendar_cboe.py
            Default: mcal_get_calendar='EUREX'

        days(bool): If True, only days of the month is outputed, else it's dataeime objects
            Default: days=True

    Output:
        dates(dict): dictionary of expiration days for each month of a specified year in datetime.date format.
    '''

    # get CBOE market holidays
    EUREXCal = mcal.get_calendar(mcal_get_calendar)
    holidays = EUREXCal.holidays().holidays

    # set calendar starting from Saturday
    c = calendar.Calendar(firstweekday=calendar.SATURDAY)

    # get the 3rd Friday of each month
    exp_dates = {}
    for i in range(1, 13):
        monthcal = c.monthdatescalendar(year, i)
        date = monthcal[2][-1]
        # check if found date is an holiday and get the previous date if it is
        if date in holidays:
            date = date + timedelta(-1)
        # append the date to the dictionary
        if year in exp_dates:
            ### Changed from original code from here on by Jonathan Legrand on 2022-10-11
            if days: exp_dates[year].append(date.day)
            else: exp_dates[year].append(date)
        else:
            if days: exp_dates[year] = [date.day]
            else: exp_dates[year] = [date]
    return exp_dates


fullDates = Get_exp_dates(2022, days=False)
dates = Get_exp_dates(2022)
fullDatesStrDict = {i: [fullDates[i][j].strftime('%Y-%m-%d')
                        for j in range(len(fullDates[i]))]
                    for i in list(fullDates.keys())}
fullDatesDayDict = {i: [fullDates[i][j].day
                        for j in range(len(fullDates[i]))]
                    for i in list(fullDates.keys())}


print(fullDates)

{2022: [datetime.date(2022, 1, 21), datetime.date(2022, 2, 18), datetime.date(2022, 3, 18), datetime.date(2022, 4, 14), datetime.date(2022, 5, 20), datetime.date(2022, 6, 17), datetime.date(2022, 7, 15), datetime.date(2022, 8, 19), datetime.date(2022, 9, 16), datetime.date(2022, 10, 21), datetime.date(2022, 11, 18), datetime.date(2022, 12, 16)]}


print(fullDatesStrDict)

{2022: ['2022-01-21', '2022-02-18', '2022-03-18', '2022-04-14', '2022-05-20', '2022-06-17', '2022-07-15', '2022-08-19', '2022-09-16', '2022-10-21', '2022-11-18', '2022-12-16']}


print(dates)

{2022: [21, 18, 18, 14, 20, 17, 15, 19, 16, 21, 18, 16]}


print(fullDatesDayDict)

{2022: [21, 18, 18, 14, 20, 17, 15, 19, 16, 21, 18, 16]}


x = 15


timeOfCalcDatetime = datetime.now()  # For now, we will focuss on the use-case where we are calculating values for today; later we will allow for it historically for any day going back a few business days.
timeOfCalcStr = datetime.now().strftime('%Y-%m-%d')
timeOfCalcStr

'2023-03-08'


fullDatesAtTimeOfCalc = Get_exp_dates(timeOfCalcDatetime.year, days=False)  # `timeOfCalcDatetime.year` here is 2023
fullDatesAtTimeOfCalcDatetime = [
    datetime(i.year, i.month, i.day)
    for i in fullDatesAtTimeOfCalc[list(fullDatesAtTimeOfCalc.keys())[0]]]


print(fullDatesAtTimeOfCalcDatetime)

[datetime.datetime(2023, 1, 20, 0, 0), datetime.datetime(2023, 2, 17, 0, 0), datetime.datetime(2023, 3, 17, 0, 0), datetime.datetime(2023, 4, 21, 0, 0), datetime.datetime(2023, 5, 19, 0, 0), datetime.datetime(2023, 6, 16, 0, 0), datetime.datetime(2023, 7, 21, 0, 0), datetime.datetime(2023, 8, 18, 0, 0), datetime.datetime(2023, 9, 15, 0, 0), datetime.datetime(2023, 10, 20, 0, 0), datetime.datetime(2023, 11, 17, 0, 0), datetime.datetime(2023, 12, 15, 0, 0)]


expiryDateOfInt = [i for i in fullDatesAtTimeOfCalcDatetime
                   if i > timeOfCalcDatetime + relativedelta(days=x)][0]
expiryDateOfInt

datetime.datetime(2023, 4, 21, 0, 0)


response2 = search.Definition(
    view=search.Views.SEARCH_ALL, # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`
    query=".STOXX50E",
    select="DocumentTitle, RIC, StrikePrice, ExchangeCode, ExpiryDate, UnderlyingAsset, " +
            "UnderlyingAssetName, UnderlyingAssetRIC, ESMAUnderlyingIndexCode, RCSUnderlyingMarket" +
            "UnderlyingQuoteName, UnderlyingQuoteRIC",
    filter="RCSAssetCategoryLeaf eq 'Option' and RIC eq 'STX*' and DocumentTitle ne '*Weekly*' " +
    "and CallPutOption eq 'Call' and ExchangeCode eq 'EUX' and " +
    f"ExpiryDate ge {(expiryDateOfInt - relativedelta(days=1)).strftime('%Y-%m-%d')} " +
    f"and ExpiryDate lt {(expiryDateOfInt + relativedelta(days=1)).strftime('%Y-%m-%d')}",  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.
    top=10000,
).get_data()
searchDf2 = response2.data.df


searchDf2


searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]]


instrument = searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]].RIC.values[0]
instrument

'STXE42750D3.EX'


## Example Options:

# instrument_1 = 'SPXv212240000.U'
# instrument_2 = 'STXE35500J2.EX'  # Eurex Dow Jones EURO STOXX 50 Index Option 3550 Call Oct 2022, Stock Index Cash Option, Underlying RIC: .STOXX50E
# instrument_3 = 'SPXj212240000.U'


datetime.now().isoformat(timespec='minutes')

'2023-03-08T11:53'


start = (timeOfCalcDatetime - pd.tseries.offsets.BDay(5)).strftime('%Y-%m-%dT%H:%M:%S.%f')  # '2022-10-05T07:30:00.000'
endDateTime = datetime.now()
end = endDateTime.strftime('%Y-%m-%dT%H:%M:%S.%f')  #  e.g.: '2022-09-09T20:00:00.000'
end

'2023-03-08T11:53:10.166926'


_RefDailyImpVolDf = historical_pricing.events.Definition(
    instrument, fields=['IMP_VOLT'], count=2000).get_data()


_RefDailyImpVolDf.data.df.head()


try: RefDailyImpVolDf = _RefDailyImpVolDf.data.df.drop(['EVENT_TYPE'], axis=1)  # In codebook, this line is needed
except: RefDailyImpVolDf = _RefDailyImpVolDf.data.df # If outside of codebook
fig = px.line(RefDailyImpVolDf, title = RefDailyImpVolDf.columns.name + " " + RefDailyImpVolDf.columns[0]) # This is just to see the implied vol graph when that field is available
fig.show()


# rd.get_history(
#     universe=["STXE35500J2.EX"],
#     fields=["TRDPRC_1"],
#     interval="tick")


_optnMrktPrice = rd.get_history(
    universe=[instrument],
    fields=["TRDPRC_1"],
    interval="10min",
    start=start,  # Ought to always start at 4 am for OPRA exchanged Options, more info in the article below
    end=end)  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below


_optnMrktPrice.head()


optnMrktPrice = _optnMrktPrice.resample('10Min').mean() # get a datapoint every 10 min
optnMrktPrice = optnMrktPrice[optnMrktPrice.index.strftime('%Y-%m-%d').isin([i for i in _optnMrktPrice.index.strftime('%Y-%m-%d').unique()])]  # Only keep trading days
optnMrktPrice = optnMrktPrice.loc[(optnMrktPrice.index.strftime('%H:%M:%S') >= '07:30:00') & (optnMrktPrice.index.strftime('%H:%M:%S') <= '22:00:00')]  # Only keep trading hours
optnMrktPrice.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values
print(f"Our dataframe started at {str(optnMrktPrice.index[0])} and went on continuously till {str(optnMrktPrice.index[-1])}, so out of trading hours rows are removed")
optnMrktPrice

Our dataframe started at 2023-03-02 08:20:00 and went on continuously till 2023-03-07 16:00:00, so out of trading hours rows are removed


# optnMrktPrice = optnMrktPrice.append(
#     pd.DataFrame(
#         [[pd.NA]], columns=optnMrktPrice.columns,
#         index=[(endDateTime + (datetime.min - endDateTime) % timedelta(minutes=10))]))
# optnMrktPrice


AToptnMrktPrice = _optnMrktPrice
AToptnMrktPrice


underlying = searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]].UnderlyingQuoteRIC.values[0][0]
underlying

'.STOXX50E'


hoursDf = rd.get_data(
    universe=["EUREX21"],
    fields=["ROW80_10"])
display(hoursDf)
hoursDf.iloc[0,1]

'       OGBL/OGBM/OGBS     07:30-08:00     08:00-19:00     19:00-20:00           '


_underlyingMrktPrice = rd.get_history(
    universe=[underlying],
    fields=["TRDPRC_1"],
    interval="10min",
    start=start,
    end=end)


_underlyingMrktPrice


ATunderlyingMrktPrice = AToptnMrktPrice.join(
    _underlyingMrktPrice, lsuffix='_OptPr', rsuffix='_UnderlyingPr', how='inner')
ATunderlyingMrktPrice


if optnMrktPrice.index[-1] >= _underlyingMrktPrice.index[-1]:
    df = optnMrktPrice.copy()
    df['underlying ' + underlying + ' TRDPRC_1'] = _underlyingMrktPrice
else:
    df = _underlyingMrktPrice.copy()
    df.rename(columns={"TRDPRC_1": 'underlying ' + underlying + ' TRDPRC_1'}, inplace=True)
    df['TRDPRC_1'] = optnMrktPrice
    df.columns.name = optnMrktPrice.columns.name
df.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values
df = df.dropna()
df


strikePrice = searchDf2.iloc[
    (searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]].StrikePrice.values[0]


strikePrice

4275


_EurRfRate = rd.get_history(
    universe=['EURIBOR3MD='],  # USD3MFSR=, USDSOFR=
    fields=['TR.FIXINGVALUE'],
    # Since we will use `dropna()` as a way to select the rows we are after later on in the code, we need to ask for more risk-free data than needed, just in case we don't have enough:
    start=(datetime.strptime(start, '%Y-%m-%dT%H:%M:%S.%f') - timedelta(days=1)).strftime('%Y-%m-%d'),
    end=(datetime.strptime(end, '%Y-%m-%dT%H:%M:%S.%f') + timedelta(days=1)).strftime('%Y-%m-%d'))


_EurRfRate


EurRfRate = _EurRfRate.resample('10Min').mean().fillna(method='ffill')
df['EurRfRate'] = EurRfRate


df = df.fillna(method='ffill')
df


pd.options.mode.chained_assignment = None  # default='warn'
ATunderlyingMrktPrice['EurRfRate'] = [pd.NA for i in ATunderlyingMrktPrice.index]
for i in _EurRfRate.index:
    _i = str(i)[:10]
    for n, j in enumerate(ATunderlyingMrktPrice.index):
        if _i in str(j):
            if len(_EurRfRate.loc[i].values) == 2:
                ATunderlyingMrktPrice['EurRfRate'].iloc[n] = _EurRfRate.loc[i].values[0][0]
            elif len(_EurRfRate.loc[i].values) == 1:
                ATunderlyingMrktPrice['EurRfRate'].iloc[n] = _EurRfRate.loc[i].values[0]
ATdf = ATunderlyingMrktPrice.copy()


ATdf = ATdf.fillna(method='ffill')
ATdf


dfGMT = df.copy()
dfLocalTimeZone = df.copy()
dfLocalTimeZone.index = [
    df.index[i].replace(
        tzinfo=pytz.timezone(
            'GMT')).astimezone(
        tz=datetime.now().astimezone().tzinfo)
    for i in range(len(df))]


dfGMT


dfLocalTimeZone


requestFields = [
    "MarketValueInDealCcy", "RiskFreeRatePercent",
    "UnderlyingPrice", "PricingModelType",
    "DividendType", "VolatilityType",
    "UnderlyingTimeStamp", "ReportCcy",
    "VolatilityType", "Volatility",
    "DeltaPercent", "GammaPercent",
    "RhoPercent", "ThetaPercent",
    "VegaPercent"]


universeL = [
        {
          "instrumentType": "Option",
          "instrumentDefinition": {
            "buySell": "Buy",
            "underlyingType": "Eti",
            "instrumentCode": instrument,
            "strike": str(strikePrice),
          },
          "pricingParameters": {
            "marketValueInDealCcy": str(dfLocalTimeZone['TRDPRC_1'][i]),
            "riskFreeRatePercent": str(dfLocalTimeZone['EurRfRate'][i]),
            "underlyingPrice": str(dfLocalTimeZone['underlying ' + underlying + ' TRDPRC_1'][i]),
            "pricingModelType": "BlackScholes",
            "dividendType": "ImpliedYield",
            "volatilityType": "Implied",
            "underlyingTimeStamp": "Default",
            "reportCcy": "EUR"
          }
        }
      for i in range(len(dfLocalTimeZone.index))]


def Chunks(lst, n):
    """Yield successive n-sized chunks from lst."""
    for i in range(0, len(lst), n):
        yield lst[i:i + n]


batchOf = 100
for i, j in enumerate(Chunks(universeL, batchOf)):
    print(f"Batch of {batchOf} requests no. {str(i+1)}/{str(len([i for i in Chunks(universeL, batchOf)]))} started")
    # Example request with Body Parameter - Symbology Lookup
    request_definition = rd.delivery.endpoint_request.Definition(
        method=rd.delivery.endpoint_request.RequestMethod.POST,
        url='https://api.refinitiv.com/data/quantitative-analytics/v1/financial-contracts',
        body_parameters={"fields": requestFields,
                         "outputs": ["Data", "Headers"],
                         "universe": j})

    response3 = request_definition.get_data()
    headers_name = [h['name'] for h in response3.data.raw['headers']]

    if i == 0:
        response3df = pd.DataFrame(
            data=response3.data.raw['data'], columns=headers_name)
    else:
        _response3df = pd.DataFrame(
            data=response3.data.raw['data'], columns=headers_name)
        response3df = response3df.append(_response3df, ignore_index=True)
    print(f"Batch of {batchOf} requests no. {str(i+1)}/{str(len([i for i in Chunks(universeL, batchOf)]))} ended")

Batch of 100 requests no. 1/3 started
Batch of 100 requests no. 1/3 ended
Batch of 100 requests no. 2/3 started
Batch of 100 requests no. 2/3 ended
Batch of 100 requests no. 3/3 started
Batch of 100 requests no. 3/3 ended


response3df


dfLocalTimeZone


CuniverseL = [  # C here is for the fact that we're using the content layer
    option.Definition(
        underlying_type=option.UnderlyingType.ETI,
        buy_sell='Buy',
        instrument_code=instrument,
        strike=float(strikePrice),
        pricing_parameters=option.PricingParameters(
            market_value_in_deal_ccy=float(dfLocalTimeZone['TRDPRC_1'][i]),
            risk_free_rate_percent=float(dfLocalTimeZone['EurRfRate'][i]),
            underlying_price=float(dfLocalTimeZone[
                'underlying ' + underlying + ' TRDPRC_1'][i]),
            pricing_model_type='BlackScholes',
            volatility_type='Implied',
            underlying_time_stamp='Default',
            report_ccy='EUR'))
    for i in range(len(dfLocalTimeZone.index))]


batchOf = 100
for i, j in enumerate(Chunks(CuniverseL, batchOf)):
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(CuniverseL, 100)])} started")
    # Example request with Body Parameter - Symbology Lookup
    response4 = rdf.Definitions(
        universe=j,
        fields=requestFields)
    response4 = response4.get_data()
    if i == 0:
        response4df = response4.data.df
    else:
        response4df = response4df.append(response4.data.df, ignore_index=True)
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(CuniverseL, 100)])} ended")

Batch of 100 requests no. 1/3 started
Batch of 100 requests no. 1/3 ended
Batch of 100 requests no. 2/3 started
Batch of 100 requests no. 2/3 ended
Batch of 28 requests no. 3/3 started
Batch of 28 requests no. 3/3 ended


IPADf = response4df.copy()  # IPA here stands for the service we used to get all the calculated valuse, Instrument Pricint Analitycs.
IPADf.index = dfLocalTimeZone.index
IPADf.columns.name = dfLocalTimeZone.columns.name
IPADf.rename(columns={"Volatility": 'ImpliedVolatility'}, inplace=True)
IPADf


ATdfGMT = ATdf.copy()
ATdfLocalTimeZone = ATdf.copy()
ATdfLocalTimeZone.index = [
    ATdf.index[i].replace(
        tzinfo=pytz.timezone(
            'GMT')).astimezone(
        tz=datetime.now().astimezone().tzinfo)
    for i in range(len(ATdf))]
ATdfGMT


ATdfLocalTimeZone


ATuniverseL = [
        {
          "instrumentType": "Option",
          "instrumentDefinition": {
            "buySell": "Buy",
            "underlyingType": "Eti",
            "instrumentCode": instrument,
            "strike": str(strikePrice),
          },
          "pricingParameters": {
            "marketValueInDealCcy": str(ATdfLocalTimeZone['TRDPRC_1_OptPr'][i]),
            "riskFreeRatePercent": str(ATdfLocalTimeZone['EurRfRate'][i]),
            "underlyingPrice": str(ATdfLocalTimeZone['TRDPRC_1_UnderlyingPr'][i]),
            "pricingModelType": "BlackScholes",
            "dividendType": "ImpliedYield",
            "volatilityType": "Implied",
            "underlyingTimeStamp": "Default",
            "reportCcy": "EUR"
          }
        }
      for i in range(len(ATdfLocalTimeZone.index))]


ATCUniverseL = [  # C here is for the fact that we're using the content layer
    option.Definition(
        underlying_type=option.UnderlyingType.ETI,
        buy_sell='Buy',
        instrument_code=instrument,
        strike=float(strikePrice),
        pricing_parameters=option.PricingParameters(
            market_value_in_deal_ccy=float(ATdfLocalTimeZone['TRDPRC_1_OptPr'][i]),
            risk_free_rate_percent=float(ATdfLocalTimeZone['EurRfRate'][i]),
            underlying_price=float(ATdfLocalTimeZone['TRDPRC_1_UnderlyingPr'][i]),
            pricing_model_type='BlackScholes',
            volatility_type='Implied',
            underlying_time_stamp='Default',
            report_ccy='EUR'))
    for i in range(len(ATdfLocalTimeZone.index))]


batchOf = 100
for i, j in enumerate(Chunks(ATCUniverseL, batchOf)):
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(ATCUniverseL, batchOf)])} started")
    # Example request with Body Parameter - Symbology Lookup
    response5 = rdf.Definitions(
        universe=j,
        fields=requestFields)
    response5 = response5.get_data()
    if i == 0:
        response5df = response5.data.df
    else:
        response5df = response5df.append(response5.data.df, ignore_index=True)
    print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(ATCUniverseL, batchOf)])} ended")

Batch of 24 requests no. 1/1 started
Batch of 24 requests no. 1/1 ended


response5df


ATIPADf = response5df.copy()  # IPA here stands for the service we used to get all the calculated valuse, Instrument Pricint Analitycs.
ATIPADf.index = ATdfLocalTimeZone.index
ATIPADf.columns.name = ATdfLocalTimeZone.columns.name
ATIPADf.rename(columns={"Volatility": 'ImpliedVolatility'}, inplace=True)
ATIPADf


display(searchDf2.iloc[
    (searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]])

IPADfGraph = IPADf[['ImpliedVolatility', 'MarketValueInDealCcy',
                    'RiskFreeRatePercent', 'UnderlyingPrice', 'DeltaPercent',
                    'GammaPercent', 'RhoPercent', 'ThetaPercent', 'VegaPercent']]

fig = px.line(IPADfGraph)  # This is just to see the implied vol graph when that field is available
# fig.layout = dict(xaxis=dict(type="category"))

# Format Graph: https://plotly.com/python/tick-formatting/
fig.update_layout(
    title=instrument,
    template='plotly_dark')

# Make it so that only one line is shown by default: # https://stackoverflow.com/questions/73384807/plotly-express-plot-subset-of-dataframe-columns-by-default-and-the-rest-as-opt
fig.for_each_trace(
    lambda t: t.update(
        visible=True if t.name in IPADfGraph.columns[:1] else "legendonly"))

# fig.update_xaxes(autorange=True)
# fig.update_layout(yaxis=IPADf.index[0::10])

fig.show()


fig = subplots.make_subplots(rows=3, cols=1)

fig.add_trace(go.Scatter(x=IPADf.index, y=IPADf.ImpliedVolatility, name='Op Imp Volatility'), row=1, col=1)
fig.add_trace(go.Scatter(x=IPADf.index, y=IPADf.MarketValueInDealCcy, name='Op Mk Pr'), row=2, col=1)
fig.add_trace(go.Scatter(x=IPADf.index, y=IPADf.UnderlyingPrice, name=underlying + ' Undrlyg Pr'), row=3, col=1)


fig.update(layout_xaxis_rangeslider_visible=False)
fig.update_layout(title=IPADf.columns.name)
fig.update_layout(
    template='plotly_dark',
    autosize=False,
    width=1300,
    height=500)
fig.show()
searchDf2.iloc[(searchDf2['StrikePrice']-currentUnderlyingPrc).abs().argsort()[:1]]


display(searchDf2.iloc[(searchDf2.StrikePrice-currentUnderlyingPrc).abs().argsort()[:1]])
ATIPADfSimpleGraph = pd.DataFrame(
    data=ATIPADf.ImpliedVolatility.values, index=ATIPADf.ImpliedVolatility.index)

fig, ax = plt.subplots(ncols=1)

ax.plot(ATIPADfSimpleGraph, '.-')
# ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))
ax.set_title(f"{searchDf2.iloc[(searchDf2.StrikePrice-currentUnderlyingPrc).abs().argsort()[:1]].RIC.values[0]} Implied Volatility At Trade Only")
fig.autofmt_xdate()

plt.show()


def ImpVolatilityCalcIPA(x=15,
                         instrument=None,
                         indexUnderlying=".STOXX50E",
                         callOrPut='Put',
                         dateBack=3,
                         expiryYearOfInterest=datetime.now().year,
                         riskFreeRate=None, riskFreeRateField=None,
                         timeZoneInGraph=datetime.now().astimezone(),
                         maxColwidth=200,
                         graphStyle='overlay',  # 'overlay', '3 graphs', 'simple'
                         simpleGraphLineStyle='.-',  # 'o-'
                         simpleGraphSize=(15, 5),
                         graphTemplate='plotly_dark',
                         debug=False,
                         returnDfGraph=False,
                         AtOptionTradeOnly=False):


    if indexUnderlying == ".STOXX50E":
        exchangeC, exchangeRIC, mcalGetCalendar = 'EUX', 'STX', 'EUREX'
    elif indexUnderlying == '.SPX':
        exchangeC, exchangeRIC, mcalGetCalendar = 'OPQ', 'SPX', 'CBOE_Futures'# 'CBOE_Index_Options'  # should be 'CBOE_Index_Options'... CBOT_Equity


    def Get_exp_dates(year=expiryYearOfInterest,
                      days=True,
                      mcal_get_calendar=mcalGetCalendar):
        '''
        Get_exp_dates Version 3.0:

        This function gets expiration dates for a year for NDX options, which are the 3rd Fridays of each month.

        Changes
        ----------------------------------------------
        Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code to allow
            (i) for the function's holiday argument to be changed, and defaulted to 'EUREX' as opposed to 'CBOE_Index_Options' and
            (ii) for the function to output full date objects as opposed to just days of the month if agument days=True.

        Changed from Version 2.0 to 3.0: Jonathan Legrand changed this function to reflec tthe fact that it can be used for indexes other than EUREX.

        Dependencies
        ----------------------------------------------
        Python library 'pandas_market_calendars' version 3.2

        Parameters
        -----------------------------------------------
        Input:
            year(int): year for which expiration days are requested

            mcal_get_calendar(str): String of the calendar for which holidays have to be taken into account. More on this calendar (link to Github chacked 2022-10-11): https://github.com/rsheftel/pandas_market_calendars/blob/177e7922c7df5ad249b0d066b5c9e730a3ee8596/pandas_market_calendars/exchange_calendar_cboe.py
                Default: mcal_get_calendar='EUREX'

            days(bool): If True, only days of the month is outputed, else it's dataeime objects
                Default: days=True

        Output:
            dates(dict): dictionary of expiration days for each month of a specified year in datetime.date format.
        '''

        # get CBOE market holidays
        Cal = mcal.get_calendar(mcal_get_calendar)
        holidays = Cal.holidays().holidays

        # set calendar starting from Saturday
        c = calendar.Calendar(firstweekday=calendar.SATURDAY)

        # get the 3rd Friday of each month
        exp_dates = {}
        for i in range(1, 13):
            monthcal = c.monthdatescalendar(year, i)
            date = monthcal[2][-1]
            # check if found date is an holiday and get the previous date if it is
            if date in holidays:
                date = date + timedelta(-1)
            # append the date to the dictionary
            if year in exp_dates:
                ### Changed from original code from here on by Jonathan Legrand on 2022-10-11
                if days: exp_dates[year].append(date.day)
                else: exp_dates[year].append(date)
            else:
                if days: exp_dates[year] = [date.day]
                else: exp_dates[year] = [date]
        return exp_dates

    timeOfCalcDatetime = datetime.now()  # For now, we will focuss on the use-case where we are calculating values for today; later we will allow for it historically for any day going back a few business days.
    timeOfCalcStr = datetime.now().strftime('%Y-%m-%d')
    fullDatesAtTimeOfCalc = Get_exp_dates(timeOfCalcDatetime.year, days=False)  # `timeOfCalcDatetime.year` here is 2023
    fullDatesAtTimeOfCalcDatetime = [
        datetime(i.year, i.month, i.day)
        for i in fullDatesAtTimeOfCalc[list(fullDatesAtTimeOfCalc.keys())[0]]]
    expiryDateOfInt = [i for i in fullDatesAtTimeOfCalcDatetime
                       if i > timeOfCalcDatetime + relativedelta(days=x)][0]

    if debug: print(f"expiryDateOfInt: {expiryDateOfInt}")

    response = search.Definition(
        view = search.Views.SEARCH_ALL, # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`
        query=indexUnderlying,
        select="DocumentTitle, RIC, StrikePrice, ExchangeCode, ExpiryDate, UnderlyingAsset, " +
                "UnderlyingAssetName, UnderlyingAssetRIC, ESMAUnderlyingIndexCode, RCSUnderlyingMarket" +
                "UnderlyingQuoteName, UnderlyingQuoteRIC",
        filter=f"RCSAssetCategoryLeaf eq 'Option' and RIC eq '{exchangeRIC}*' and DocumentTitle ne '*Weekly*' " +
        f"and CallPutOption eq '{callOrPut}' and ExchangeCode eq '{exchangeC}' and " +
        f"ExpiryDate ge {(expiryDateOfInt - relativedelta(days=1)).strftime('%Y-%m-%d')} " +
        f"and ExpiryDate lt {(expiryDateOfInt + relativedelta(days=1)).strftime('%Y-%m-%d')}",  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.
        top=10000,
    ).get_data()
    searchDf = response.data.df

    if debug: display(searchDf)

    try:
        underlyingPrice =  rd.get_history(
            universe=[indexUnderlying],
            fields=["TRDPRC_1"],
            interval="tick").iloc[-1][0]
    except:
        print("Function failed at the search strage, returning the following dataframe: ")
        display(searchDf)

    if debug:
        print(f"Underlying {indexUnderlying}'s price recoprded here was {underlyingPrice}")
        display(searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:10]])

    if instrument is None:
        instrument = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:1]].RIC.values[0]

    start = (timeOfCalcDatetime - pd.tseries.offsets.BDay(dateBack)).strftime('%Y-%m-%dT%H:%M:%S.%f')  # '2022-10-05T07:30:00.000'
    endDateTime = datetime.now()
    end = endDateTime.strftime('%Y-%m-%dT%H:%M:%S.%f')  #  e.g.: '2022-09-09T20:00:00.000'

    _optnMrktPrice = rd.get_history(
        universe=[instrument],
        fields=["TRDPRC_1"],
        interval="10min",
        start=start,  # Ought to always start at 4 am for OPRA exchanged Options, more info in the article below
        end=end)  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below

    if debug:
        print(instrument)
        display(_optnMrktPrice)

    ## Data on certain options are stale and do not nessesarily show up on Workspace, in case that happens, we will pick the next ATM Option, which probably will have the same strike, but we will only do so once, any more and we could get too far from strike:
    if _optnMrktPrice.empty:
        if debug: print(f"No data could be found for {instrument}, so the next ATM Option was chosen")
        instrument = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[1:2]].RIC.values[0]
        if debug: print(f"{instrument}")
        _optnMrktPrice = rd.get_history(universe=[instrument],
                                        fields=["TRDPRC_1"], interval="10min",
                                        start=start, end=end)
        if debug: display(_optnMrktPrice)
    if _optnMrktPrice.empty:  # Let's try one more time, as is often nessesary
        if debug: print(f"No data could be found for {instrument}, so the next ATM Option was chosen")
        instrument = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[2:3]].RIC.values[0]
        if debug: print(f"{instrument}")
        _optnMrktPrice = rd.get_history(universe=[instrument],
                                        fields=["TRDPRC_1"], interval="10min",
                                        start=start, end=end)
        if debug: display(_optnMrktPrice)
    if _optnMrktPrice.empty:
        print(f"No data could be found for {instrument}, please check it on Refinitiv Workspace")

    optnMrktPrice = _optnMrktPrice.resample('10Min').mean() # get a datapoint every 10 min
    optnMrktPrice = optnMrktPrice[optnMrktPrice.index.strftime('%Y-%m-%d').isin([i for i in _optnMrktPrice.index.strftime('%Y-%m-%d').unique()])]  # Only keep trading days
    optnMrktPrice = optnMrktPrice.loc[(optnMrktPrice.index.strftime('%H:%M:%S') >= '07:30:00') & (optnMrktPrice.index.strftime('%H:%M:%S') <= '22:00:00')]  # Only keep trading hours
    optnMrktPrice.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values

    # Note also that one may want to only look at 'At Option Trade' datapoints,
    # i.e.: Implied Volatility when a trade is made for the Option, but not when
    # none is made. For this, we will use the 'At Trade' (`AT`) dataframes:
    if AtOptionTradeOnly: AToptnMrktPrice = _optnMrktPrice

    underlying = searchDf.iloc[(searchDf.StrikePrice).abs().argsort()[:1]].UnderlyingQuoteRIC.values[0][0]

    _underlyingMrktPrice = rd.get_history(
        universe=[underlying],
        fields=["TRDPRC_1"],
        interval="10min",
        start=start,
        end=end)
    # Let's put it al in one data-frame, `df`. Some datasets will have data
    # going from the time we sert for `start` all the way to `end`. Some won't
    # because no trade happened in the past few minutes/hours. We ought to base
    # ourselves on the dataset with values getting closer to `end` and `ffill`
    # for the other column. As a result, the following `if` loop is needed:
    if optnMrktPrice.index[-1] >= _underlyingMrktPrice.index[-1]:
        df = optnMrktPrice.copy()
        df[f"underlying {underlying} TRDPRC_1"] = _underlyingMrktPrice
    else:
        df = _underlyingMrktPrice.copy()
        df.rename(
            columns={"TRDPRC_1": f"underlying {underlying} TRDPRC_1"},
            inplace=True)
        df['TRDPRC_1'] = optnMrktPrice
        df.columns.name = optnMrktPrice.columns.name
    df.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values
    df = df.dropna()

    if AtOptionTradeOnly:
        ATunderlyingMrktPrice = AToptnMrktPrice.join(
            _underlyingMrktPrice, lsuffix='_OptPr', how='inner',
            rsuffix=f" Underlying {underlying} TRDPRC_1")
        ATdf = ATunderlyingMrktPrice

    strikePrice = searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:1]].StrikePrice.values[0]

    if riskFreeRate is None and indexUnderlying == ".SPX":
        _riskFreeRate = 'USDCFCFCTSA3M='
        _riskFreeRateField = 'TR.FIXINGVALUE'
    elif riskFreeRate is None and indexUnderlying == ".STOXX50E":
        _riskFreeRate = 'EURIBOR3MD='
        _riskFreeRateField = 'TR.FIXINGVALUE'
    else:
        _riskFreeRate, _riskFreeRateField = riskFreeRate, riskFreeRateField

    _RfRate = rd.get_history(
        universe=[_riskFreeRate],  # USD3MFSR=, USDSOFR=
        fields=[_riskFreeRateField],
        # Since we will use `dropna()` as a way to select the rows we are after later on in the code, we need to ask for more risk-free data than needed, just in case we don't have enough:
        start=(datetime.strptime(start, '%Y-%m-%dT%H:%M:%S.%f') - timedelta(days=1)).strftime('%Y-%m-%d'),
        end=(datetime.strptime(end, '%Y-%m-%dT%H:%M:%S.%f') + timedelta(days=1)).strftime('%Y-%m-%d'))
    RfRate = _RfRate.resample('10Min').mean().fillna(method='ffill')
    if AtOptionTradeOnly:
        pd.options.mode.chained_assignment = None  # default='warn'
        ATunderlyingMrktPrice['RfRate'] = [pd.NA for i in ATunderlyingMrktPrice.index]
        for i in RfRate.index:
            _i = str(i)[:10]
            for n, j in enumerate(ATunderlyingMrktPrice.index):
                if _i in str(j):
                    if len(RfRate.loc[i].values) == 2:
                        ATunderlyingMrktPrice['RfRate'].iloc[n] = RfRate.loc[i].values[0][0]
                    elif len(RfRate.loc[i].values) == 1:
                        ATunderlyingMrktPrice['RfRate'].iloc[n] = RfRate.loc[i].values[0]
        ATdf = ATunderlyingMrktPrice.copy()
        ATdf = ATdf.fillna(method='ffill')  # This is in case there were no Risk Free datapoints released after a certain time, but trades on the option still went through.
    else:
        df['RfRate'] = RfRate
        df = df.fillna(method='ffill')

    if timeZoneInGraph != 'GMT' and AtOptionTradeOnly:
        ATdf.index = [
            ATdf.index[i].replace(
                tzinfo=pytz.timezone(
                    'GMT')).astimezone(
                tz=datetime.now().astimezone().tzinfo)
            for i in range(len(ATdf))]
    elif timeZoneInGraph != 'GMT':
        df.index = [
            df.index[i].replace(
                tzinfo=pytz.timezone(
                    'GMT')).astimezone(
                tz=timeZoneInGraph.tzinfo)
            for i in range(len(df))]

    if AtOptionTradeOnly:
        if debug:
            print("ATdf")
            display(ATdf)
        universeL = [
            option.Definition(
                underlying_type=option.UnderlyingType.ETI,
                buy_sell='Buy',
                instrument_code=instrument,
                strike=float(strikePrice),
                pricing_parameters=option.PricingParameters(
                    market_value_in_deal_ccy=float(ATdf.TRDPRC_1_OptPr[i]),
                    risk_free_rate_percent=float(ATdf.RfRate[i]),
                    underlying_price=float(ATdf[
                        f"TRDPRC_1 Underlying {underlying} TRDPRC_1"][i]),
                    pricing_model_type='BlackScholes',
                    volatility_type='Implied',
                    underlying_time_stamp='Default',
                    report_ccy='EUR'))
            for i in range(len(ATdf.index))]
    else:
        if debug:
            print("df")
            display(df)
        universeL = [  # C here is for the fact that we're using the content layer
            option.Definition(
                underlying_type=option.UnderlyingType.ETI,
                buy_sell='Buy',
                instrument_code=instrument,
                strike=float(strikePrice),
                pricing_parameters=option.PricingParameters(
                    market_value_in_deal_ccy=float(df.TRDPRC_1[i]),
                    risk_free_rate_percent=float(df.RfRate[i]),
                    underlying_price=float(df[
                        f"underlying {underlying} TRDPRC_1"][i]),
                    pricing_model_type='BlackScholes',
                    volatility_type='Implied',
                    underlying_time_stamp='Default',
                    report_ccy='EUR'))
            for i in range(len(df.index))]

    def Chunks(lst, n):
        """Yield successive n-sized chunks from lst."""
        for i in range(0, len(lst), n):
            yield lst[i:i + n]

    requestFields = [
        "MarketValueInDealCcy", "RiskFreeRatePercent",
        "UnderlyingPrice", "PricingModelType",
        "DividendType", "VolatilityType",
        "UnderlyingTimeStamp", "ReportCcy",
        "VolatilityType", "Volatility",
        "DeltaPercent", "GammaPercent",
        "RhoPercent", "ThetaPercent", "VegaPercent"]

    batchOf = 100
    for i, j in enumerate(Chunks(universeL, batchOf)):
        if debug: print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(universeL, batchOf)])} started")
        # Example request with Body Parameter - Symbology Lookup
        request_definition = rdf.Definitions(universe=j, fields=requestFields)
        response = request_definition.get_data()
        if i == 0:
            IPADf = response.data.df
        else:
            IPADf = IPADf.append(response.data.df, ignore_index=True)
        if debug: print(f"Batch of {len(j)} requests no. {i+1}/{len([i for i in Chunks(universeL, batchOf)])} ended")

    if AtOptionTradeOnly:
        IPADf.index = ATdf.index
        IPADf.columns.name = ATdf.columns.name
    else:
        IPADf.index = df.index
        IPADf.columns.name = df.columns.name
    IPADf.rename(columns={"Volatility": 'ImpliedVolatility'}, inplace=True)

    # We are going to want to show details about data retreived in a dataframe in the output of this function. The one line below allows us to maximise the width (column) length of cells to see all that is written within them.
    pd.options.display.max_colwidth = maxColwidth

    if graphStyle == 'simple':
        display(searchDf.iloc[(searchDf.StrikePrice-underlyingPrice).abs().argsort()[:1]])
        fig, axes = plt.subplots(ncols=1, figsize=simpleGraphSize)
        axes.plot(
            pd.DataFrame(  # Unfortunutally, Matplotlib, which is the library used here for simple graphs, require our dataframe to be in a specific format that necessitate the use of `pd.DataFrame`
                data=IPADf[['ImpliedVolatility']].ImpliedVolatility.values,
                index=IPADf[['ImpliedVolatility']].ImpliedVolatility.index),
            simpleGraphLineStyle)
        if AtOptionTradeOnly: axes.set_title(f"{instrument} Implied Volatility At Trade Only")
        else: axes.set_title(f"{instrument} Implied Volatility")
        plt.show()

    else:

        display(searchDf.iloc[(searchDf['StrikePrice']-underlyingPrice).abs().argsort()[:1]])

        IPADfGraph = IPADf[
            ['ImpliedVolatility', 'MarketValueInDealCcy',
             'RiskFreeRatePercent', 'UnderlyingPrice', 'DeltaPercent',
             'GammaPercent', 'RhoPercent', 'ThetaPercent', 'VegaPercent']]

        if debug: display(IPADfGraph)

        try:  # This is needed in case there is not enough data to calculate values for all timestamps , see https://stackoverflow.com/questions/67244912/wide-format-csv-with-plotly-express
            fig = px.line(IPADfGraph)
        except:
            if returnDfGraph:
                return IPADfGraph
            else:
                IPADfGraph = IPADfGraph[
                    ["ImpliedVolatility", "MarketValueInDealCcy",
                     "RiskFreeRatePercent", "UnderlyingPrice"]]
                fig = px.line(IPADfGraph)

        if graphStyle == 'overlay':
            fig.update_layout(
                title=instrument,
                template=graphTemplate)
            fig.for_each_trace(
                lambda t: t.update(
                    visible=True if t.name in IPADfGraph.columns[:1] else "legendonly"))
            fig.show()

        elif graphStyle == '3 graphs':
            fig = plotly.subplots.make_subplots(rows=3, cols=1)

            fig.add_trace(go.Scatter(
                x=IPADf.index, y=IPADfGraph.ImpliedVolatility,
                name='Op Imp Volatility'), row=1, col=1)
            fig.add_trace(go.Scatter(
                x=IPADf.index, y=IPADfGraph.MarketValueInDealCcy,
                name='Op Mk Pr'), row=2, col=1)
            fig.add_trace(go.Scatter(
                x=IPADf.index, y=IPADfGraph.UnderlyingPrice,
                name=f"{underlying} Undrlyg Pr"), row=3, col=1)

            fig.update(layout_xaxis_rangeslider_visible=False)
            fig.update_layout(title=IPADfGraph.columns.name)
            fig.update_layout(
                title=instrument,
                template=graphTemplate,
                autosize=False,
                width=1300,
                height=500)
            fig.show()

        else:

            print("Looks like the agrument `graphStyle` used is incorrect. Try `simple`, `overlay` or `3 graphs`")


ImpVolatilityCalcIPA(  # This will pick up 10 min data
    x=15,
    indexUnderlying=".SPX",  # ".SPX" or ".STOXX50E"
    callOrPut='Call',  # 'Put' or 'Call'
    dateBack=7,
    expiryYearOfInterest=datetime.now().year,
    riskFreeRate=None,
    riskFreeRateField=None,  # 'TR.FIXINGVALUE'
    timeZoneInGraph=datetime.now().astimezone(),
    maxColwidth=200,
    graphStyle='overlay',  # 'overlay', '3 graphs', 'simple'
    simpleGraphLineStyle='.-',  # 'o-'
    simpleGraphSize=(15, 5),
    graphTemplate='plotly_dark',
    debug=False,
    returnDfGraph=True,
    AtOptionTradeOnly=True)


# while True:
#     # Code executed here
#     clear_output(wait=True)
#     ImpVolatilityCalcIPA(
#                 dateBack=3, indexUnderlying=".STOXX50E", callOrPut='Call',
#                 graphStyle='simple', AtOptionTradeOnly=True)
#             time.sleep(5)


timeOfCalc2 = "2022-04-01"
indexUnderlying2 = ".STOXX50E"


timeOfCalcDatetime2 = datetime.strptime(timeOfCalc2, '%Y-%m-%d')
currentUnderlyingPrc2 = rd.get_history(
    universe=[indexUnderlying2],
    start=timeOfCalc2,  # , end: "OptDateTime"=None
    fields=["TRDPRC_1"],
    interval="tick").iloc[-1][0]
currentUnderlyingPrc2

4282.63


if indexUnderlying2 == ".STOXX50E":
    exchangeC2, exchangeRIC2, mcalGetCalendar2 = "EUX", "STX", "EUREX"
elif indexUnderlying == ".SPX":
    exchangeC2, exchangeRIC2, mcalGetCalendar2 = "OPQ", "SPX", "CBOE_Futures"
exchangeC2, exchangeRIC2, mcalGetCalendar2

('EUX', 'STX', 'EUREX')


fullDatesAtTimeOfCalc2 = Get_exp_dates(
    year=2022,
    days=False,
    mcal_get_calendar=mcalGetCalendar2)
fullDatesAtTimeOfCalc2

{2022: [datetime.date(2022, 1, 21),
  datetime.date(2022, 2, 18),
  datetime.date(2022, 3, 18),
  datetime.date(2022, 4, 14),
  datetime.date(2022, 5, 20),
  datetime.date(2022, 6, 17),
  datetime.date(2022, 7, 15),
  datetime.date(2022, 8, 19),
  datetime.date(2022, 9, 16),
  datetime.date(2022, 10, 21),
  datetime.date(2022, 11, 18),
  datetime.date(2022, 12, 16)]}


fullDatesAtTimeOfCalcDatetime2 = [
    datetime(i.year, i.month, i.day)
    for i in fullDatesAtTimeOfCalc2[list(fullDatesAtTimeOfCalc2.keys())[0]]]
fullDatesAtTimeOfCalcDatetime2

[datetime.datetime(2022, 1, 21, 0, 0),
 datetime.datetime(2022, 2, 18, 0, 0),
 datetime.datetime(2022, 3, 18, 0, 0),
 datetime.datetime(2022, 4, 14, 0, 0),
 datetime.datetime(2022, 5, 20, 0, 0),
 datetime.datetime(2022, 6, 17, 0, 0),
 datetime.datetime(2022, 7, 15, 0, 0),
 datetime.datetime(2022, 8, 19, 0, 0),
 datetime.datetime(2022, 9, 16, 0, 0),
 datetime.datetime(2022, 10, 21, 0, 0),
 datetime.datetime(2022, 11, 18, 0, 0),
 datetime.datetime(2022, 12, 16, 0, 0)]


expiryDateOfInt2 = [i for i in fullDatesAtTimeOfCalcDatetime2
                    if i > timeOfCalcDatetime2 + relativedelta(days=x)][0]
expiryDateOfInt2

datetime.datetime(2022, 5, 20, 0, 0)


def Get_exp_month(exp_date, opt_type):

    # define option expiration identifiers
    ident = {
        '1':  {'exp': 'A', 'C': 'A', 'P': 'M'},
        '2':  {'exp': 'B', 'C': 'B', 'P': 'N'},
        '3':  {'exp': 'C', 'C': 'C', 'P': 'O'},
        '4':  {'exp': 'D', 'C': 'D', 'P': 'P'},
        '5':  {'exp': 'E', 'C': 'E', 'P': 'Q'},
        '6':  {'exp': 'F', 'C': 'F', 'P': 'R'},
        '7':  {'exp': 'G', 'C': 'G', 'P': 'S'},
        '8':  {'exp': 'H', 'C': 'H', 'P': 'T'},
        '9':  {'exp': 'I', 'C': 'I', 'P': 'U'},
        '10': {'exp': 'J', 'C': 'J', 'P': 'V'},
        '11': {'exp': 'K', 'C': 'K', 'P': 'W'},
        '12': {'exp': 'L', 'C': 'L', 'P': 'X'}}

    # get expiration month code for a month
    if opt_type.upper() == 'C':
        exp_month = ident[str(exp_date.month)]['C']
    elif opt_type.upper() == 'P':
        exp_month = ident[str(exp_date.month)]['P']

    return ident, exp_month


def Check_ric(ric, maturity, ident):
    exp_date = pd.Timestamp(maturity)

    # get start and end date for get_historical_price_summaries
    # query (take current date minus 90 days period)
    sdate = (datetime.now() - timedelta(90)).strftime('%Y-%m-%d')
    edate = datetime.now().strftime('%Y-%m-%d')

    # check if option is matured. If yes, add expiration syntax and recalculate
    # start and end date of the query (take expiration day minus 90 days period)
    if pd.Timestamp(maturity) < datetime.now():
        ric = ric + '^' + ident[str(exp_date.month)]['exp'] + str(exp_date.year)[-2:]
        sdate = (exp_date - timedelta(90)).strftime('%Y-%m-%d')
        edate = exp_date.strftime('%Y-%m-%d')

    # request option prices. Please note, there is no settle price for OPRA traded options
    fieldsRequest = ['BID', 'ASK', 'TRDPRC_1']
    if not ric.split('.')[1][0] == 'U':
        fieldsRequest.append('SETTLE')

    prices = rd.content.historical_pricing.summaries.Definition(
        ric, start=sdate, end=edate,
        interval=rd.content.historical_pricing.Intervals.DAILY,
        fields=fieldsRequest).get_data()

    return ric, prices


def Get_ric_eurex(asset, maturity, strike, opt_type):
    exp_date = pd.Timestamp(maturity)

    if asset[0] == '.':
        asset_name = asset[1:]
        if asset_name == 'FTSE':
            asset_name = 'OTUK'
        elif asset_name == 'SSMI':
            asset_name = 'OSMI'
        elif asset_name == 'GDAXI':
            asset_name = 'GDAX'
        elif asset_name == 'ATX':
            asset_name = 'FATXA'
        elif asset_name == 'STOXX50E':
            asset_name = 'STXE'
    else:
        asset_name = asset.split('.')[0]

    ident, exp_month = Get_exp_month(
        exp_date=exp_date, opt_type=opt_type)

    if type(strike) == float:
        int_part = int(strike)
        dec_part = str(str(strike).split('.')[1])[0]
    else:
        int_part = int(strike)
        dec_part = '0'

    if len(str(int(strike))) == 1:
        strike_ric = '0' + str(int_part) + dec_part
    else:
        strike_ric = str(int_part) + dec_part

    possible_rics = []
    generations = ['', 'a', 'b', 'c', 'd']
    for gen in generations:
        ric = asset_name + strike_ric + gen + exp_month + str(exp_date.year)[-1:] + '.EX'
        ric, prices = Check_ric(ric, maturity, ident)
        if prices is not None:
            return ric, prices
        else:
            possible_rics.append(ric)
    print(f'Here is a list of possible RICs {possible_rics}, however we could not find any prices for those!')
    return ric, prices


int(round(currentUnderlyingPrc2, -2))

4300


instrument2, instrument2Prices = Get_ric_eurex(
    asset='.STOXX50E', opt_type='P',
    maturity=expiryDateOfInt2.strftime('%Y-%m-%d'),
    strike=int(round(currentUnderlyingPrc2, -2)))


instrument2

'STXE43000Q2.EX^E22'


instrument2Prices.data.df


# response = search.Definition(
#         view=search.Views.SEARCH_ALL, # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`
#         query=indexUnderlying,
#         select="DocumentTitle, RIC, StrikePrice, ExchangeCode, ExpiryDate, UnderlyingAsset, \
#                 UnderlyingAssetName, UnderlyingAssetRIC, ESMAUnderlyingIndexCode, RCSUnderlyingMarket, \
#                 UnderlyingQuoteName, UnderlyingQuoteRIC",
#         filter=f"RCSAssetCategoryLeaf eq 'Option' and RIC eq '{exchangeRIC}*' and DocumentTitle ne '*Weekly*' " +
#         f"and CallPutOption eq '{callOrPut}' and ExchangeCode eq '{exchangeC}' and " +
#         f"ExpiryDate ge {(expiryDateOfInt - relativedelta(days=1)).strftime('%Y-%m-%d')} " +
#         f"and ExpiryDate lt {(expiryDateOfInt + relativedelta(days=1)).strftime('%Y-%m-%d')}",  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.
#         top=10000
#     ).get_data()
#     searchDf = response.data.df


# def Get_exchange_code(asset):

#     # build search query to find exchange codes where the option on the given underlying is traded  
#     response = search.Definition(
# #         view=search.Views.SEARCH_ALL,  # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`
#         query=asset,
#         filter="SearchAllCategory eq 'Options' and Periodicity eq 'Monthly' ",
#         select=' RIC, DocumentTitle, UnderlyingQuoteRIC,Periodicity, ExchangeCode',
#         navigators="ExchangeCode",
#         top=10000
#     ).get_data()
#     result = response.data.raw["Navigators"]["ExchangeCode"]

#     # store a=exchange codes in a list
#     exchange_codes = []
#     for i in range(len(result['Buckets'])):
#         code = result['Buckets'][i]['Label']
#         exchange_codes.append(code)
#     return exchange_codes


def Get_exchanges(asset, topNuSearchResults=10_000):

    response = search.Definition(
        query=asset,
        filter="SearchAllCategory eq 'Options' and Periodicity eq 'Monthly' ",
        select=' RIC, DocumentTitle, UnderlyingQuoteRIC,Periodicity, ExchangeCode',
        navigators="ExchangeCode",
        top=topNuSearchResults).get_data()
    result = response.data.raw["Navigators"]["ExchangeCode"]

    exchange_codes = []
    for i in range(len(result['Buckets'])):
        code = result['Buckets'][i]['Label']
        exchange_codes.append(code)
    return exchange_codes


from typing import Tuple, Union, Dict, List, Any


class Option_RIC:
    """
    Option_RIC
    """

    def __init__(
        self,
        maturity: str,  # '2022-01-21'
        strike: int,
        opt_type: str,  # 'C' or 'P'
        asset: str = ".STOXX50E",
        debug: bool = False,
        topNuSearchResults: int = 10_000):

        # Most objects are simple to define at this stage, but soon you'll see that two of them are a little more finicky
        self.maturity = pd.Timestamp(maturity)
        self.strike = strike
        self.opt_type = opt_type
        self.debug = debug
        self.asset = asset

        response = search.Definition(
            query=asset,
            filter="SearchAllCategory eq 'Options' and Periodicity eq 'Monthly' ",
            select=' RIC, DocumentTitle, UnderlyingQuoteRIC,Periodicity, ExchangeCode',
            navigators="ExchangeCode",
            top=topNuSearchResults).get_data()
        result = response.data.raw["Navigators"]["ExchangeCode"]
        exchange_codes = []
        for i in range(len(result['Buckets'])):
            code = result['Buckets'][i]['Label']
            exchange_codes.append(code)

        self.exchange = exchange_codes


    def Check_ric(self, ric, maturity):
        """
        Support Function used within other functions in `Option_RIC` Class.
        """

        exp_date = pd.Timestamp(maturity)

        if pd.Timestamp(maturity) < datetime.now():
            sdate = (exp_date - timedelta(90)).strftime('%Y-%m-%d')
            edate = exp_date.strftime('%Y-%m-%d')
        else:
            sdate = (datetime.now() - timedelta(90)).strftime('%Y-%m-%d')
            edate = datetime.now().strftime('%Y-%m-%d')

        if ric.split('.')[1][0] == 'U':
            prices = rd.content.historical_pricing.summaries.Definition(
                ric,  start=sdate, end=edate,
                interval=rd.content.historical_pricing.Intervals.DAILY,
                fields=['BID', 'ASK', 'TRDPRC_1']).get_data()
        else:
            prices = rd.content.historical_pricing.summaries.Definition(
                ric,  start=sdate, end=edate,
                interval=rd.content.historical_pricing.Intervals.DAILY,
                fields=['BID', 'ASK', 'TRDPRC_1', 'SETTLE']).get_data()
        if self.debug:
            print(f"Check_ric's (sdate, edate) = ({sdate}, {edate})")
        return ric, prices

    def Get_asset_and_exchange(self):
        """
        Support Function used within other functions in `Option_RIC` Class.
        """

        asset_in_ric: Dict[str, Dict[Union[str, Dict[str, str]]]] = {
            'SSMI': {'EUX': 'OSMI'},
            'GDAXI': {'EUX': 'GDAX'},
            'ATX': {'EUX': 'FATXA'},
            'STOXX50E': {'EUX': 'STXE'},
            'FTSE': {'IEU': 'LFE', 'EUX': 'OTUK'},
            'N225': {'OSA': 'JNI'},
            'TOPX': {'OSA': 'JTI'}}

        asset_exchange: Dict[str, str] = {}
        if self.asset[0] != '.':
            asset: str = self.asset.split('.')[0]
        else:
            asset: str = self.asset[1:]
        for exch in self.exchange:
            if asset in asset_in_ric:
                asset_exchange[exch] = asset_in_ric[asset][exch]
            else:
                asset_exchange[exch] = asset
        return asset_exchange

    def Get_strike(self, exch):

        if exch == 'OPQ':
            if type(self.strike) == float:
                int_part = int(self.strike)
                dec_part = str(str(self.strike).split('.')[1])
            else:
                int_part = int(self.strike)
                dec_part = '00'
            if int(self.strike) < 10:
                strike_ric = '00' + str(int_part) + dec_part
            elif int_part >= 10 and int_part < 100:
                strike_ric = '0' + str(int_part) + dec_part
            elif int_part >= 100 and int_part < 1000:
                strike_ric = str(int_part) + dec_part
            elif int_part >= 1000 and int_part < 10000:
                strike_ric = str(int_part) + '0'
            elif int_part >= 10000 and int_part < 20000:
                strike_ric = 'A' + str(int_part)[-4:]
            elif int_part >= 20000 and int_part < 30000:
                strike_ric = 'B' + str(int_part)[-4:]
            elif int_part >= 30000 and int_part < 40000:
                strike_ric = 'C' + str(int_part)[-4:]
            elif int_part >= 40000 and int_part < 50000:
                strike_ric = 'D' + str(int_part)[-4:]

        elif exch == 'HKG' or exch == 'HFE':
            if self.asset[0] == '.':
                strike_ric = str(int(self.strike))
            else:
                strike_ric = str(int(self.strike * 100))

        elif exch == 'OSA':
            strike_ric = str(self.strike)[:3]

        elif exch == 'EUX' or exch == 'IEU':
            if type(self.strike) == float and len(str(int(self.strike))) == 1:
                int_part = int(self.strike)
                dec_part = str(str(self.strike).split('.')[1])[0]
                strike_ric = '0' + str(int_part) + dec_part
            elif (len(str(int(self.strike))) > 1 and exch == 'EUX'):
                strike_ric = str(int(self.strike)) + '0'
            elif (len(str(int(self.strike))) == 2 and exch == 'IEU'):
                strike_ric = '0' + str(int(self.strike))
            elif len(str(int(self.strike))) > 2 and exch == 'IEU':
                strike_ric = str(int(self.strike))

        return strike_ric

    def Get_exp_month(self, exchange):
        """
        Support Function used within other functions in `Option_RIC` Class.
        """
        ident_opra = {
            '1': {'exp': 'A', 'C_bigStrike': 'a', 'C_smallStrike': 'A',
                  'P_bigStrike': 'm', 'P_smallStrike': 'M'},
            '2': {'exp': 'B', 'C_bigStrike': 'b', 'C_smallStrike': 'B',
                  'P_bigStrike': 'n', 'P_smallStrike': 'N'},
            '3': {'exp': 'C', 'C_bigStrike': 'c', 'C_smallStrike': 'C',
                  'P_bigStrike': 'o', 'P_smallStrike': 'O'},
            '4': {'exp': 'D', 'C_bigStrike': 'd', 'C_smallStrike': 'D',
                  'P_bigStrike': 'p', 'P_smallStrike': 'P'},
            '5': {'exp': 'E', 'C_bigStrike': 'e', 'C_smallStrike': 'E',
                  'P_bigStrike': 'q', 'P_smallStrike': 'Q'},
            '6': {'exp': 'F', 'C_bigStrike': 'f', 'C_smallStrike': 'F',
                  'P_bigStrike': 'r', 'P_smallStrike': 'R'},
            '7': {'exp': 'G', 'C_bigStrike': 'g', 'C_smallStrike': 'G',
                  'P_bigStrike': 's', 'P_smallStrike': 'S'},
            '8': {'exp': 'H', 'C_bigStrike': 'h', 'C_smallStrike': 'H',
                  'P_bigStrike': 't', 'P_smallStrike': 'T'},
            '9': {'exp': 'I', 'C_bigStrike': 'i', 'C_smallStrike': 'I',
                  'P_bigStrike': 'u', 'P_smallStrike': 'U'},
            '10': {'exp': 'J', 'C_bigStrike': 'j', 'C_smallStrike': 'J',
                   'P_bigStrike': 'v', 'P_smallStrike': 'V'},
            '11': {'exp': 'K', 'C_bigStrike': 'k', 'C_smallStrike': 'K',
                   'P_bigStrike': 'w', 'P_smallStrike': 'W'},
            '12': {'exp': 'L', 'C_bigStrike': 'l', 'C_smallStrike': 'L',
                   'P_bigStrike': 'x', 'P_smallStrike': 'X'}}

        ident_all = {
            '1':  {'exp': 'A', 'C': 'A', 'P': 'M'},
            '2':  {'exp': 'B', 'C': 'B', 'P': 'N'},
            '3':  {'exp': 'C', 'C': 'C', 'P': 'O'},
            '4':  {'exp': 'D', 'C': 'D', 'P': 'P'},
            '5':  {'exp': 'E', 'C': 'E', 'P': 'Q'},
            '6':  {'exp': 'F', 'C': 'F', 'P': 'R'},
            '7':  {'exp': 'G', 'C': 'G', 'P': 'S'},
            '8':  {'exp': 'H', 'C': 'H', 'P': 'T'},
            '9':  {'exp': 'I', 'C': 'I', 'P': 'U'},
            '10': {'exp': 'J', 'C': 'J', 'P': 'V'},
            '11': {'exp': 'K', 'C': 'K', 'P': 'W'},
            '12': {'exp': 'L', 'C': 'L', 'P': 'X'}}

        if exchange == 'OPQ':
            if self.strike > 999.999:
                exp_month_code = ident_opra[str(
                    self.maturity.month)][self.opt_type + '_bigStrike']
            else:
                exp_month_code = ident_opra[str(
                    self.maturity.month)][self.opt_type + '_smallStrike']
        else:
            exp_month_code = ident_all[str(self.maturity.month)][self.opt_type]

        if self.maturity < datetime.now():
            expired = '^' + \
                ident_all[str(self.maturity.month)]['exp'] + \
                str(self.maturity.year)[-2:]
        else:
            expired = ''

        return exp_month_code, expired

    def RIC_prices(self, ric, ricPrices):
        if self.debug:
            print(f"ricPrices's ric: {ric}")
            print(f"self.maturity: {self.maturity}")
        ric, prices = self.Check_ric(ric, self.maturity)
        if prices is not None:
            valid_ric = {ric: prices}
            ricPrices['valid_ric'].append(valid_ric)
        else:
            ricPrices['potential_rics'].append(ric)

        return ricPrices

    def Construct_RIC(self):
        asset_exchange = self.Get_asset_and_exchange()
        supported_exchanges = ['OPQ', 'IEU', 'EUX', 'HKG', 'HFE', 'OSA']
        ricPrices = {'valid_ric': [], 'potential_rics': []}
        for exchange, asset in asset_exchange.items():
            if exchange in supported_exchanges:
                strike_ric = self.Get_strike(exchange)
                exp_month_code, expired = self.Get_exp_month(exchange)

                if exchange == 'OPQ':
                    ric = asset + exp_month_code + \
                        str(self.maturity.day) + \
                        str(self.maturity.year)[-2:] + \
                        strike_ric + '.U' + expired
                    ricPrices = self.RIC_prices(ric, ricPrices)

                elif exchange == 'HKG' or exchange == 'HFE':
                    gen_len = ['0', '1', '2', '3']
                    if exchange == 'HFE':
                        gen_len = ['']
                    for i in gen_len:
                        exchs = {'HKG': {'exch_code': '.HK', 'gen': str(i)},
                                 'HFE': { 'exch_code': '.HF', 'gen': ''}}
                        ric = asset + strike_ric + exchs[exchange]['gen'] + exp_month_code + str(
                            self.maturity.year)[-1:] + exchs[exchange]['exch_code'] + expired
                        ricPrices = self.RIC_prices(ric, ricPrices)

                elif exchange == 'OSA':
                    for jnet in ['', 'L', 'R']:
                        if self.asset[0] == '.':
                            ric = asset + jnet + strike_ric + exp_month_code + \
                                str(self.maturity.year)[-1:] + '.OS' + expired
                            ricPrices = self.RIC_prices(ric, ricPrices)
                        else:
                            for gen in ['Y', 'Z', 'A', 'B', 'C']:
                                ric = asset + jnet + gen + strike_ric + exp_month_code + \
                                    str(self.maturity.year)[-1:] + \
                                    '.OS' + expired
                                ricPrices = self.RIC_prices(ric, ricPrices)

                elif exchange == 'EUX' or exchange == 'IEU':
                    exchs = {'EUX': '.EX', 'IEU': '.L'}
                    for gen in ['', 'a', 'b', 'c', 'd']:
                        ric = asset + strike_ric + gen + exp_month_code + \
                            str(self.maturity.year)[-1:] + \
                            exchs[exchange] + expired
                        if self.debug: print(f"Construct_RIC's ric: {ric}")
                        try:
                            ricPrices = self.RIC_prices(ric, ricPrices)
                        except:
                            if self.debug:
                                print("Error for self.RIC_prices(ric, ricPrices)")
            else:
                print(f'The {exchange} exchange is not supported yet')
        return ricPrices


timeOfCalc2 = '2021-01-21'
indexUnderlying2 = ".STOXX50E"


timeOfCalcDatetime2 = datetime.strptime(timeOfCalc2, '%Y-%m-%d')
currentUnderlyingPrc2 = rd.get_history(
    universe=[indexUnderlying2],
    start=timeOfCalc2,  # , end: "OptDateTime"=None
    fields=["TRDPRC_1"],
    interval="tick").iloc[-1][0]
currentUnderlyingPrc2

4282.63


STOXX50ETest1 = Option_RIC(
    maturity=timeOfCalc2,
    strike=int(round(currentUnderlyingPrc2, -2)),
    opt_type='P',
    asset=indexUnderlying2,
    debug=False)


STOXX50ETest2 = STOXX50ETest1.Construct_RIC()


list(STOXX50ETest2['valid_ric'][0].keys())[0]

'STXE43000M1.EX^A21'


STOXX50ETest2['valid_ric'][0][list(STOXX50ETest2['valid_ric'][0].keys())[0]].data.df.head()


timeOfCalc2 + 'T11:53:10.166926'

'2021-01-21T11:53:10.166926'


STOXX50ETest2OptnMrktPrice = rd.get_history(
    universe=list(STOXX50ETest2['valid_ric'][0].keys()),
    fields=["TRDPRC_1"],
    interval="10min",
    start=timeOfCalc2 + 'T11:53:09.168706',
    end=timeOfCalc2 + 'T11:53:10.166926')  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below
STOXX50ETest2OptnMrktPrice


timeOfCalc3 = '2022-02-10'
indexUnderlying3 = ".SPX"
timeOfCalcDatetime3 = datetime.strptime(timeOfCalc3, '%Y-%m-%d')
currentUnderlyingPrc3 = rd.get_history(
    universe=[indexUnderlying3],
    start=timeOfCalc3,  # , end: "OptDateTime"=None
    fields=["TRDPRC_1"],
    interval="tick").iloc[-1][0]
currentUnderlyingPrc3

3986.37


SPXTest2 = Option_RIC(
    maturity='2022-01-21',
    strike=int(round(currentUnderlyingPrc3, -2)),
    opt_type='P',
    asset=indexUnderlying3,
    debug=False)
SPXTest2 = SPXTest2.Construct_RIC()


list(SPXTest2['valid_ric'][0].keys())[0]

'SPXm212240000.U^A22'


SPXTest2['valid_ric'][0][list(SPXTest2['valid_ric'][0].keys())[0]].data.df


SPXTest2OptnMrktPrice = rd.get_history(
    universe=list(SPXTest2['valid_ric'][0].keys())[0],
    fields=["TRDPRC_1"],
    interval="10min",
    start=timeOfCalc2 + 'T11:53:09.168706',
    end=timeOfCalc2 + 'T11:53:10.166926')  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below
SPXTest2OptnMrktPrice


timeOfCalc2+'T11:53:09.168706'

'2021-01-21T11:53:09.168706'


rd.content.historical_pricing.summaries.Definition(
    universe ='STXE43000M1.EX^A21',
    start='2021-01-23',
    end='2021-01-29',
#     fields=['BID', 'ASK', 'TRDPRC_1', 'SETTLE']
    interval=rd.content.historical_pricing.Intervals.DAILY).get_data()

---------------------------------------------------------------------------
RDError                                   Traceback (most recent call last)
Cell In[125], line 1
----> 1 rd.content.historical_pricing.summaries.Definition(
      2     universe ='STXE43000M1.EX^A21',
      3     start='2021-01-23',
      4     end='2021-01-29',
      5 #     fields=['BID', 'ASK', 'TRDPRC_1', 'SETTLE']
      6     interval=rd.content.historical_pricing.Intervals.DAILY).get_data()

File C:\JupyterLab\resources\jlab_server\lib\site-packages\refinitiv\data\delivery\_data\_data_provider.py:666, in DataProviderLayer.get_data(self, session, on_response, **kwargs)
    664 api_config = get_api_config(data_type, config)
    665 auto_retry = api_config.get("auto-retry", False)
--> 666 response = self._provider.get_data(
    667     session, url, auto_retry=auto_retry, **kwargs, **self._kwargs
    668 )
    669 on_response and emit_event(on_response, response, self, session)
    670 self._check_response(response, config)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\refinitiv\data\content\_content_provider.py:258, in HistoricalDataProvider.get_data(self, *args, **kwargs)
    255             raise exception
    256         responses.append(fut.result())
--> 258 validate_responses(responses)
    259 return self._join_responses(responses, universe, fields, kwargs)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\refinitiv\data\content\_content_provider.py:297, in validate_responses(responses)
    295             error_message += f"({error.code}, {sub_error_message}), "
    296 error_message = error_message[:-2]
--> 297 raise RDError(1, f"No data to return, please check errors: {error_message}")

RDError: Error code 1 | No data to return, please check errors: ERROR: No successful response.
(None, None)


rd.content.historical_pricing.summaries.Definition(
    universe ='STXE43000M1.EX^A21',
    start='2021-01-23T11:53:09.168706',
    end='2021-01-26T19:53:10.166926',
#     fields=['BID', 'ASK', 'TRDPRC_1', 'SETTLE']
    interval=rd.content.historical_pricing.Intervals.MINUTE).get_data()

---------------------------------------------------------------------------
RDError                                   Traceback (most recent call last)
Cell In[122], line 1
----> 1 rd.content.historical_pricing.summaries.Definition(
      2     universe ='STXE43000M1.EX^A21	',
      3     start='2021-01-26T11:53:09.168706',
      4     end='2021-01-26T19:53:10.166926',
      5 #     fields=['BID', 'ASK', 'TRDPRC_1', 'SETTLE']
      6     interval=rd.content.historical_pricing.Intervals.MINUTE).get_data()

File C:\JupyterLab\resources\jlab_server\lib\site-packages\refinitiv\data\delivery\_data\_data_provider.py:666, in DataProviderLayer.get_data(self, session, on_response, **kwargs)
    664 api_config = get_api_config(data_type, config)
    665 auto_retry = api_config.get("auto-retry", False)
--> 666 response = self._provider.get_data(
    667     session, url, auto_retry=auto_retry, **kwargs, **self._kwargs
    668 )
    669 on_response and emit_event(on_response, response, self, session)
    670 self._check_response(response, config)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\refinitiv\data\content\_content_provider.py:258, in HistoricalDataProvider.get_data(self, *args, **kwargs)
    255             raise exception
    256         responses.append(fut.result())
--> 258 validate_responses(responses)
    259 return self._join_responses(responses, universe, fields, kwargs)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\refinitiv\data\content\_content_provider.py:297, in validate_responses(responses)
    295             error_message += f"({error.code}, {sub_error_message}), "
    296 error_message = error_message[:-2]
--> 297 raise RDError(1, f"No data to return, please check errors: {error_message}")

RDError: Error code 1 | No data to return, please check errors: ERROR: No successful response.
(None, None)


rd.content.historical_pricing.Intervals.MINUTE


help(rd.content.historical_pricing.summaries.Definition

Help on class Definition in module refinitiv.data.content.historical_pricing._summaries_definition:

class Definition(refinitiv.data.delivery._data._data_provider.DataProviderLayer)
 |  Definition(*args, **kwds)
 |  
 |  Summary line of this class that defines parameters for requesting summaries from historical pricing
 |  
 |  Parameters
 |  ----------
 |  universe : str or list of str
 |      The entity universe
 |  interval : str or Intervals, optional
 |      The consolidation interval in ISO8601
 |  start : str or date or datetime or timedelta, optional
 |      The start date and timestamp of the query in ISO8601 with UTC only
 |  end : str or date or datetime or timedelta, optional
 |      The end date and timestamp of the query in ISO8601 with UTC only
 |  adjustments : list of Adjustments or Adjustments or str, optional
 |      The adjustment list or Adjustments type
 |  sessions : list of MarketSession or MarketSession or str, optional
 |      The list of market session classification or str
 |  count : int, optional
 |      The maximum number of data returned. Values range: 1 - 10000
 |  fields : list, optional
 |      The list of fields that are to be returned in the response
 |  closure : Any, optional
 |      Specifies the parameter that will be merged with the request
 |  extended_params : dict, optional
 |      If necessary other parameters
 |  
 |  Examples
 |  --------
 |  >>> from refinitiv.data.content.historical_pricing import summaries
 |  >>> definition_summaries = summaries.Definition("EUR")
 |  >>> response = definition_summaries.get_data()
 |  
 |  Method resolution order:
 |      Definition
 |      refinitiv.data.delivery._data._data_provider.DataProviderLayer
 |      typing.Generic
 |      builtins.object
 |  
 |  Methods defined here:
 |  
 |  __init__(self, universe: Union[str, List[str]], interval: Union[str, refinitiv.data.content._intervals.Intervals] = None, start: Union[str, datetime.date, datetime.datetime, datetime.timedelta, NoneType] = None, end: Union[str, datetime.date, datetime.datetime, datetime.timedelta, NoneType] = None, adjustments: Union[List[refinitiv.data.content.historical_pricing._hp_data_provider.Adjustments], refinitiv.data.content.historical_pricing._hp_data_provider.Adjustments, str] = None, sessions: Union[List[refinitiv.data.content.historical_pricing._hp_data_provider.MarketSession], refinitiv.data.content.historical_pricing._hp_data_provider.MarketSession, str] = None, count: Union[int, NoneType] = None, fields: Union[list, NoneType] = None, closure: Union[Any, NoneType] = None, extended_params: Union[dict, NoneType] = None) -> None
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |  
 |  __orig_bases__ = (refinitiv.data.delivery._data._data_provider.Dat...f...
 |  
 |  __parameters__ = ()
 |  
 |  ----------------------------------------------------------------------
 |  Methods inherited from refinitiv.data.delivery._data._data_provider.DataProviderLayer:
 |  
 |  __repr__(self)
 |      Return repr(self).
 |  
 |  get_data(self, session=None, on_response=None, **kwargs) -> ~T
 |  
 |  async get_data_async(self, session=None, on_response=None, **kwargs) -> ~T
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors inherited from refinitiv.data.delivery._data._data_provider.DataProviderLayer:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  ----------------------------------------------------------------------
 |  Class methods inherited from typing.Generic:
 |  
 |  __class_getitem__(params) from builtins.type
 |  
 |  __init_subclass__(*args, **kwargs) from builtins.type
 |      This method is called when a class is subclassed.
 |      
 |      The default implementation does nothing. It may be
 |      overridden to extend subclasses.
 |  
 |  ----------------------------------------------------------------------
 |  Static methods inherited from typing.Generic:
 |  
 |  __new__(cls, *args, **kwds)
 |      Create and return a new object.  See help(type) for accurate signature.


def get_potential_rics(year, trans_day, asset, OTM_size, diff, opt_type):
    '''
    This function returns the list of potential option RICs for a specified year reconstructed based on Refinitiv RIC and option trading rules.
    Dependencies
    ----------------------------------------------
    Python library 'Refinitiv Dataplatform' version 1.0.0a8.post1
    
    Parameters
    -----------------------------------------------
    Input:
        year (int): year for which transaction days are requested
        trans_day (str, default = 'first'): takes either 'first' or 'third' indicating to the first business day or the 3rd Friday of a month respectively
        asset (str): RIC code of the asset
        OTM_size (int): percentage number indicating how far away is the strike price from the price of the underlying asset
        diff (int): Tolarated difference in OTM to construct upper and lower bounds of strike prices
        opt_type (str): takes either "call" or "put"
        
    Output:
        potential_RICs (dict): dictionary containing potential RICs for each month with strike prices from the lower to upper bounds of strikes
        strikes (list): list of the strike prices calculated based on OTM size for each month
    '''
        
    # open file to report log of the function output
    report = open("Log report.txt", "a")
    
    # call functions to get expiration and transaction days
    trans_days = get_trans_days(year, trans_day)
    trans_days_prev = get_trans_days(year - 1, trans_day)
    dates = get_exp_dates(year) 
    
    # trim underlying asset's RIC to get the required part for option RIC
    if asset[0] == '.': # check if the asset is an index or an equity
        asset_name = asset[1:] # get the asset name - we remove "." symbol for index options
        adj_factors = 12 * [1] # set adjustment factors to be equal to 1 for each month (no stock split corporate event is applicable to indices)
    else:
        asset_name = asset.split('.')[0] # we need only the first part of the RICs for equities
        # get list of corporate events for equities
        corp_event = rdp.get_data(universe = asset,
                                  fields = ["TR.CAEffectiveDate", "TR.CAAdjustmentFactor", "TR.CAAdjustmentType"],
                                  parameters = {"CAEventType": "SSP", "SDate": datetime.today().strftime("%Y-%m-%d"), "EDate": "-50Y"})
        # run adjustment_factor function to get the factors
        adj_factors = adjustment_factor(corp_event, year = year, trans_day = trans_day)
        
    # define expiration month codes to be used after "^" sign
    exp = ['A', 'B', 'C', 'D','E','F','G', 'H', 'I', 'J', 'K', 'L']
    potential_RICs = {}
    strikes = []
    
    # construct potential RICs for each month of a specified year
    for j in range(1,13):
        # get day of expiration for a month
        day = dates[year][j - 1]
        # get date of price request, which is in the previous month of expiration 
        if j != 1:
            date = str(year) + '-' + str(j - 1) + '-' + str(trans_days[j - 2])
        if j == 1: # for January, we need to subtract a year along with the month
            date = str(year - 1) + '-' + str(j + 11) + '-' + str(trans_days_prev[j + 10])    
        # get price of underlying asset as of the transaction date
        
        # get the corresponding adjustment factor for the month
        adj_factor = adj_factors[j-1]
        
        price = rdp.get_data(asset, fields = ['TR.PriceClose'], parameters = {'SDate': date})
        price = float(price.iloc[0,1]) / adj_factor # adjust prices by the adjustment factor. if no sptick split events adj_factor = 1
                
        # calculate the strike price for call options
        if opt_type.lower() == 'call':
            strike = price + price * OTM_size / 100
            # define expiration month codes for call options while also considering the strike price
            if strike > 999.999:
                exp_codes_call = ['a', 'b', 'c', 'd','e','f','g', 'h', 'i', 'j', 'k', 'l']
            else:
                exp_codes_call = ['A', 'B', 'C', 'D','E','F','G', 'H', 'I', 'J', 'K', 'L']
            # get expiration month code for a month
            exp_month = exp_codes_call[j-1]

        # calculate the strike price and get expiration month code for a month for put options
        elif opt_type.lower() == 'put':
            strike = price - price * OTM_size/100
            if strike > 999.999:
                exp_codes_put = ['m', 'n', 'o', 'p','q','r','s', 't', 'u', 'v', 'w', 'x'] 
            else:
                exp_codes_put = ['M', 'N', 'O', 'P','Q','R','S', 'T', 'U', 'V', 'W', 'X']
            exp_month = exp_codes_put[j-1]
            
        strikes.append(int(round(strike,0))) # append the calculated strike price to the list of strikes
        
        # calculate lower and upper bounds for strikes considering the value of the strike
        if strike > 999.999:
            step = 5 # we loop over strikes with a step 5 for larger strikes
            strike_ub = int(round((strike + strike * diff / 100),-1))
            strike_lb = int(round((strike - strike * diff / 100),-1))
        else:
            step = 1 # we loop over strikes with a step 1 for smaller strikes
            strike_ub = int(strike + strike * diff / 100)
            strike_lb = int(strike - strike * diff / 100)
        
        # construct RICs for each strike from the lower to upper bound ranges of strikes
        for n in range(strike_lb, strike_ub + step, step):
            k = None # for strikes < 1000 along with 1 step increment change in strikes we do 0.5 point increment change which 
                     #allows us to consider strikes with decimal points. This is important to get closer OTMs for smaller valued assets.
            # here we construct option RICs by adding together all the RIC components
            # Please note some of the components are different depending on the strike value
            plc_holdr1 = asset_name + exp_month + str(day) + str(year)[-2:]
            plc_holdr2 = exp[j - 1] + str(year)[-2:]
            if n < 10:
                z = plc_holdr1 + '00' + str(n) + '00.U^' + plc_holdr2# for integer steps
                k = plc_holdr1 + '00' + str(n) + '50.U^' + plc_holdr2# for decimal steps
            elif n >= 10 and n < 100:
                z = plc_holdr1 + '0' + str(n) + '00.U^' + plc_holdr2
                k = plc_holdr1 + '0' + str(n) + '50.U^' + plc_holdr2
            if n >= 100 and n < 1000:
                z = plc_holdr1 + str(n) + '00.U^' + plc_holdr2  
                k = plc_holdr1 + str(n) + '50.U^' + plc_holdr2
            elif n >= 1000 and n < 10000:
                z = plc_holdr1 + str(n) + '0.U^' + plc_holdr2
            elif n >= 10000 and n < 20000:
                z = plc_holdr1 + 'A' + str(n)[-4:] + '.U^' + plc_holdr2
            elif n >= 20000 and n < 30000:
                z = plc_holdr1 + 'B' + str(n)[-4:] + '.U^' + plc_holdr2
            elif n >= 30000 and n < 40000:
                z = plc_holdr1 + 'C' + str(n)[-4:] + '.U^' + plc_holdr2
            elif n >= 40000 and n < 50000:
                z = plc_holdr1 + 'D' + str(n)[-4:] + '.U^' + plc_holdr2
            
            # append RICs with integer strikes to the dictionary
            if j in potential_RICs:
                potential_RICs[j].append(z)
                # append RICs with decimal point strikes to the dictionary
                if k is not None:
                    potential_RICs[j].append(k)
            else:
                potential_RICs[j] = [z]
                if k is not None:
                    potential_RICs[j].append(k)  
                    
    # report funtion results and close the log file
    now = {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
    report.write(f'{now}: Potential RICs for {opt_type} options with {OTM_size}% OTM for {year} are constructed\n')
    report.close()
    return potential_RICs, strikes


import nb_mypy  # !pip3 install nb_mypy --trusted-host pypi.org # https://pypi.org/project/nb-mypy/ # https://gitlab.tue.nl/jupyter-projects/nb_mypy/-/blob/master/Nb_Mypy.ipynb


%load_ext nb_mypy

Version 1.0.4


%reload_ext nb_mypy

Version 1.0.4


%nb_mypy On


%nb_mypy DebugOff


# %nb_mypy unknown


from datetime import date as dtdate
import pandas_market_calendars as mcal  # See `https://github.com/rsheftel/pandas_market_calendars/blob/master/examples/usage.ipynb` for info on this market calendar library
from typing import Tuple, Union, Dict, List, Any
import numpy as np
import calendar
from __future__ import annotations  # This native library allows us to use not-yet-fully-deffined classes as a Type Hint when inside that class.


import refinitiv.data as rd  # This is LSEG's Data and Analytics' API wrapper, called the Refinitiv Data Library for Python.
from refinitiv.data.content import historical_pricing  # We will use this Python Class in `rd` to show the Implied Volatility data already available before our work.
from refinitiv.data.content import search  # We will use this Python Class in `rd` to fid the instrument we are after, closest to At The Money.
from refinitiv.data.content.ipa.financial_contracts import option  # We're going to need thtis to use the content layer of the RD library and the calculators of greeks and Impl Volat in IPA & ETI
import refinitiv.data.content.ipa.financial_contracts as rdf  # We're going to need thtis to use the content layer of the RD library and the calculators of greeks and Impl Volat in Instrument Pricing Analytics (IPA) and Exchange Traded Instruments (ETI)

import numpy as np  # We need `numpy` for mathematical and array manipilations.
import pandas as pd  # We need `pandas` for datafame and array manipilations.
import calendar  # We use `calendar` to identify holidays and maturity dates of intruments of interest.
import pytz  # We use `pytz` to manipulate time values aiding `calendar` library. to import its types, you might need to run `!python3 -m pip install types-pytz`
import pandas_market_calendars as mcal  # Used to identify holidays. See `https://github.com/rsheftel/pandas_market_calendars/blob/master/examples/usage.ipynb` for info on this market calendar library
from datetime import datetime, timedelta, timezone  # We use these to manipulate time values
from dateutil.relativedelta import relativedelta  # We use `relativedelta` to manipulate time values aiding `calendar` library.

# `plotly` is a library used to render interactive graphs:
import plotly
import plotly.graph_objects as go
import plotly.express as px  # This is just to see the implied vol graph when that field is available
import matplotlib.pyplot as plt  # We use `matplotlib` to just in case users do not have an environment suited to `plotly`.
from IPython.display import display, clear_output  # We use `clear_output` for users who wish to loop graph production on a regular basis.

# Let's authenticate ourseves to LSEG's Data and Analytics service, Refinitiv:
try:  # The following libraries are not available in Codebook, thus this try loop
    rd.open_session(config_name="C:\\Example.DataLibrary.Python-main\\Example.DataLibrary.Python-main\\Configuration\\refinitiv-data.config.json")
    rd.open_session("desktop.workspace")
except:
    rd.open_session()
print(f"Here we are using the refinitiv Data Library version {rd.__version__}")

<cell>10: error: Library stubs not installed for "pytz"  [import]
<cell>10: note: Hint: "python3 -m pip install types-pytz"
<cell>13: error: Library stubs not installed for "dateutil.relativedelta"  [import]
<cell>13: note: Hint: "python3 -m pip install types-python-dateutil"
<cell>13: note: (or run "mypy --install-types" to install all missing stub packages)
<cell>13: note: See https://mypy.readthedocs.io/en/stable/running_mypy.html#missing-imports

Here we are using the refinitiv Data Library version 1.0.0b20


class IndexImpVolatNGreeksIPACalc():  # All about Type Hints here: https://realpython.com/python-type-checking/#static-type-checking

    def __init__(  # Constroctor
        self,
        indexUnderlying: str=".STOXX50E"
    ):

        self.indexUnderlying: str=indexUnderlying
        # self.expiryYearOfInterest: int = datetime.now().year
        # self.riskFreeRate: Union[str, None] = None
        # self.riskFreeRateField: Union[str, None] = None
        # self.graphStyle: str = 'without out of trading hours'  # 'overlay', '3 graphs', 'simple'
        # self.graphTemplate: str = 'plotly_dark'
        # self.debug: bool = False
        # self.returnDfGraph: bool = False
        # # def change_attrs(self, **kwargs):  for kwarg in kwargs:    self.__setattr__(kwarg, kwargs[kwarg])

    def Get_exp_dates(
        self,
        year: int=datetime.now().year,
        days: bool=True,
        mcal_get_calendar: str='EUREX'
    ) -> Dict[int, Union[dtdate, str]]:
        '''
        Get_exp_dates Version 4.0:

        This function gets expiration dates for a year for NDX options, which are the 3rd Fridays of each month.

        Changes
        ----------------------------------------------
        Changed from Version 1.0 to 2.0: Jonathan Legrand changed Haykaz Aramyan's original code to allow
            (i) for the function's holiday argument to be changed, and defaulted to 'EUREX' as opposed to 'CBOE_Index_Options' and
            (ii) for the function to output full date objects as opposed to just days of the month if agument days=True.

        Changed from Version 2.0 to 3.0: Jonathan Legrand changed this function to reflec the fact that it can be used for indexes other than EUREX.

        Changed from Version 3.0 to 4.0: Jonathan Legrand changed this function to be in line with PEP 3107 (type hints).

        Dependencies
        ----------------------------------------------
        Python library 'pandas_market_calendars' version 3.2

        Parameters
        -----------------------------------------------
        Input:
            year(int): year for which expiration days are requested

            mcal_get_calendar(str): String of the calendar for which holidays have to be taken into account. More on this calendar (link to Github chacked 2022-10-11): https://github.com/rsheftel/pandas_market_calendars/blob/177e7922c7df5ad249b0d066b5c9e730a3ee8596/pandas_market_calendars/exchange_calendar_cboe.py
                Default: mcal_get_calendar='EUREX'

            days(bool): If True, only days of the month is outputed, else it's dataeime objects
                Default: days=True

        Output
        -----------------------------------------------
            dates(dict): dictionary of expiration days for each month of a specified year in datetime.date format.
        '''

        i: int  # this is for the 'for loop' in this function coming below

        # get CBOE market holidays
        Cal: mcal.get_calendar=mcal.get_calendar(mcal_get_calendar)
        holidays: Tuple[np.datetime64, ...]=Cal.holidays().holidays

        # set calendar starting from Saturday
        c: calendar.Calendar=calendar.Calendar(firstweekday=calendar.SATURDAY)

        # get the 3rd Friday of each month
        exp_dates: dict={}  # https://stackoverflow.com/questions/48054521/indicating-multiple-value-in-a-dict-for-type-hints
        date: dtdate
        for i in range(1, 13):
            date = c.monthdatescalendar(year, i)[2][-1]
            # check if found date is an holiday and get the previous date if it is
            if date in holidays:
                date = date + timedelta(-1)
            # append the date to the dictionary
            if year in exp_dates and days:
                exp_dates[year].append(date.day)
            elif year in exp_dates:
                exp_dates[year].append(date)
            elif days:
                exp_dates[year] = [date.day]
            else:
                exp_dates[year] = [date]

        return exp_dates

    def Search_index_opt_ATM(
        self,
        debug: bool = False,
        after: int = 15,
        callOrPut: str = 'Put',
        searchFields: List[str] = ["ExchangeCode", "UnderlyingQuoteName"],
        includeWeeklyOpts: bool = False,
        topNuSearchResults: int = 10_000,
        timeOfCalcDatetime: datetime = datetime.now(),  # Here we allow for historical analysis.
        underMrktPriceField: str = "TRDPRC_1"
    ) -> IndexImpVolatNGreeksIPACalc:

        self.after = after
        self.timeOfCalcDatetime = timeOfCalcDatetime
        self.underMrktPriceField = underMrktPriceField
        i: int; j: dtdate; k: str  # this is for the 'for loop' in this function coming below

        self.exchangeC: str; self.exchangeRIC: str; self.mcalGetCalendar: str
        if self.indexUnderlying == ".STOXX50E":
            self.exchangeC, self.exchangeRIC, self.mcalGetCalendar = 'EUX', 'STX', 'EUREX'
        elif self.indexUnderlying == '.SPX':
            self.exchangeC, self.exchangeRIC, self.mcalGetCalendar = 'OPQ', 'SPX', 'CBOE_Futures'  # 'CBOE_Index_Options'  # should be 'CBOE_Index_Options'... CBOT_Equity

        timeOfCalcStr: str=timeOfCalcDatetime.strftime('%Y-%m-%d')
        fullDatesAtTimeOfCalc: dict = self.Get_exp_dates(
            year=timeOfCalcDatetime.year,
            days=False,
            mcal_get_calendar=self.mcalGetCalendar)
        fullDatesAtTimeOfCalcDatetime: List[datetime] = [
            datetime(j.year, j.month, j.day)
            for j in fullDatesAtTimeOfCalc[
                list(fullDatesAtTimeOfCalc.keys())[0]]]
        expiryDateOfInt: datetime = [
            j for j in fullDatesAtTimeOfCalcDatetime
            if j > timeOfCalcDatetime + relativedelta(days=self.after)][0]

        if debug: print(f"expiryDateOfInt: {expiryDateOfInt}")

        # Certain search fields are nessesary for the next steps, so let's add them to the `searchFields` object:
        for k in ['DocumentTitle', 'RIC', 'StrikePrice', 'UnderlyingQuoteRIC'][::-1]:  # the `[::-1]` reverses the list
            searchFields.insert(0, k)

        # Now let's build our Search filter:
        _filter: str = f"RCSAssetCategoryLeaf eq 'Option' \
                        and RIC eq '{self.exchangeRIC}*' \
                        and CallPutOption eq '{callOrPut}' \
                        and ExchangeCode eq '{self.exchangeC}' \
                        and ExpiryDate ge {(expiryDateOfInt - relativedelta(days=1)).strftime('%Y-%m-%d')} \
                        and ExpiryDate lt {(expiryDateOfInt + relativedelta(days=1)).strftime('%Y-%m-%d')}"
        if not includeWeeklyOpts:
            _filter += " and DocumentTitle ne '*Weekly*'"

        response1 = search.Definition(
            view=search.Views.SEARCH_ALL,  # To see what views are available: `help(search.Views)` & `search.metadata.Definition(view = search.Views.SEARCH_ALL).get_data().data.df.to_excel("SEARCH_ALL.xlsx")`
            query=self.indexUnderlying,
            select=', '.join(map(str, searchFields)),
            filter=_filter,  # ge (greater than or equal to), gt (greater than), lt (less than) and le (less than or equal to). These can only be applied to numeric and date properties.
            top=topNuSearchResults,
        ).get_data()
        self.searchDf: pd.DataFrame = response1.data.df
        searchDf: pd.DataFrame = self.searchDf

        if debug:
            print("searchDf")
            display(searchDf)

        try:
            self.underlyingPrice: str = rd.get_history(
                universe=[self.indexUnderlying],
                fields=[underMrktPriceField],
                interval="tick").iloc[-1][0]
        except:
            print("Function failed at the search strage, returning the following dataframe: ")
            display(searchDf)

        if debug:
            print(f"Underlying {self.indexUnderlying}'s price recorded here was {self.underlyingPrice}")
            display(searchDf.iloc[(searchDf.StrikePrice-self.underlyingPrice).abs().argsort()[:10]])

        self.instrument: str = searchDf.iloc[(
            searchDf.StrikePrice-self.underlyingPrice).abs().argsort()[:1]].RIC.values[0]
        self.instrumentInfo: pd.DataFrame = searchDf.iloc[(
            searchDf.StrikePrice-self.underlyingPrice).abs().argsort()[:1]]
        self.ATMOpt = self.instrument

        return self

    def IPA_calc(
        self,
        dateBack: int = 3,
        optnMrktPriceField: str = "TRDPRC_1",
        debug: bool = False,
        atOptionTradeOnly: bool = True,
        riskFreeRate: Union[str, None] = None,
        riskFreeRateField: Union[str, None] = None,
        timeZoneInGraph: datetime = datetime.now().astimezone(),
        requestFields: List[str] = [
            "DeltaPercent", "GammaPercent", "RhoPercent",
            "ThetaPercent", "VegaPercent"],
        searchBatchMax: int = 100
    ) -> IndexImpVolatNGreeksIPACalc:

        i: int  # Type Hinted for loops coming up below.
        k: str  # Type Hinted for loops coming up below.
        n: int  # Type Hinted for loops coming up below.
        m: int  # Type Hinted for loops coming up below.
        p: rdf._base_definition.BaseDefinition  # Type Hinted for loops coming up below.
        self.dateBack: int = dateBack
        self.start: dtdate = self.timeOfCalcDatetime - pd.tseries.offsets.BDay(
            self.dateBack)
        self.startStr: str = (self.timeOfCalcDatetime - pd.tseries.offsets.BDay(
            self.dateBack)).strftime('%Y-%m-%dT%H:%M:%S.%f')  # e.g.: '2022-10-05T07:30:00.000'
        self.endStr: str = self.timeOfCalcDatetime.strftime('%Y-%m-%dT%H:%M:%S.%f')

        _optnMrktPrice: pd.DataFrame = rd.get_history(
            universe=[self.instrument],
            fields=[optnMrktPriceField],
            interval="10min",
            start=self.startStr,  # Ought to always start at 4 am for OPRA exchanged Options, more info in the article below
            end=self.endStr)  # Ought to always end at 8 pm for OPRA exchanged Options, more info in the article below

        if _optnMrktPrice.empty:
            print(f"No data could be found for {self.instrument}, please check it on Refinitiv Workspace")

        if debug:
            print(self.instrument)
            display(_optnMrktPrice)

        # get a datapoint every 10 min
        optnMrktPrice: pd.DataFrame = _optnMrktPrice.resample(
            '10Min').mean()
        # Only keep trading days
        self.optnMrktPrice: pd.DataFrame = optnMrktPrice[
            optnMrktPrice.index.strftime('%Y-%m-%d').isin(
                [k for k in _optnMrktPrice.index.strftime('%Y-%m-%d').unique()])]
        # Forward Fill to populate NaN values
        self.optnMrktPrice.fillna(method='ffill', inplace=True)

        # Note also that one may want to only look at 'At Option Trade' datapoints,
        # i.e.: Implied Volatility when a trade is made for the Option, but not when
        # none is made. For this, we will use the 'At Trade' (`AT`) dataframes:
        if atOptionTradeOnly:
            self.AToptnMrktPrice: pd.DataFrame = _optnMrktPrice

        self.underlying: str = self.searchDf.iloc[
            (self.searchDf.StrikePrice).abs().argsort()[
                :1]].UnderlyingQuoteRIC.values[0][0]

        _underlyingMrktPrice: pd.DataFrame = rd.get_history(
            universe=[self.underlying],
            fields=[self.underMrktPriceField],
            interval="10min",
            start=self.startStr,
            end=self.endStr)

        # Let's put it al in one data-frame, `df`. Some datasets will have data
        # going from the time we set for `startStr` all the way to `endStr`. Some won't
        # because no trade happened in the past few minutes/hours. We ought to base
        # ourselves on the dataset with values getting closer to `end` and `ffill`
        # for the other column. As a result, the following `if` loop is needed:
        if optnMrktPrice.index[-1] >= _underlyingMrktPrice.index[-1]:
            df: pd.DataFrame = self.optnMrktPrice.copy()
            df[f"underlying {self.underlying} {self.underMrktPriceField}"] = _underlyingMrktPrice
        else:
            df = _underlyingMrktPrice.copy()
            df.rename(
                columns={self.underMrktPriceField:
                         f"underlying {self.underlying} {self.underMrktPriceField}"},
                inplace=True)
            df[self.underMrktPriceField] = self.optnMrktPrice
            df.columns.name = self.optnMrktPrice.columns.name
        df.fillna(method='ffill', inplace=True)  # Forward Fill to populate NaN values
        selfdf: pd.DataFrame = df.dropna()

        if atOptionTradeOnly:
            ATunderlyingMrktPrice: pd.DataFrame = self.AToptnMrktPrice.join(
                _underlyingMrktPrice,
                rsuffix=f"_{self.underlying}_underlying",
                lsuffix=f"_{self.instrument}_OptPr",
                how='inner')

        self.strikePrice: pd.DataFrame = self.searchDf.iloc[
            (self.searchDf['StrikePrice']-self.underlyingPrice).abs().argsort()[
                :1]].StrikePrice.values[0]

        # I didn't think that I needed to Type Hint for the event when
        # `_riskFreeRate` & `_riskFreeRateField` were `None`, but Error Messages
        # suggest otherwise...
        _riskFreeRate: Union[str, None]
        _riskFreeRateField: Union[str, None]
        if riskFreeRate is None and self.indexUnderlying == ".SPX":
            _riskFreeRate, _riskFreeRateField = 'USDCFCFCTSA3M=', 'TR.FIXINGVALUE'
        elif riskFreeRate is None and self.indexUnderlying == ".STOXX50E":
            _riskFreeRate, _riskFreeRateField = 'EURIBOR3MD=', 'TR.FIXINGVALUE'
        elif riskFreeRate is not None:
            _riskFreeRate, _riskFreeRateField = riskFreeRate, riskFreeRateField
        self.riskFreeRate: Union[str, None] = riskFreeRate
        self.riskFreeRateField: Union[str, None] = riskFreeRateField

        _RfRate: pd.DataFrame = rd.get_history(
            universe=[_riskFreeRate],  # USD3MFSR=, USDSOFR=
            fields=[_riskFreeRateField],
            # Since we will use `dropna()` as a way to select the rows we are after later on in the code, we need to ask for more risk-free data than needed, just in case we don't have enough:
            start=(self.start - timedelta(days=1)).strftime('%Y-%m-%d'),  # https://teamtreehouse.com/community/local-variable-datetime-referenced-before-assignment
            end=(self.timeOfCalcDatetime +
                 timedelta(days=1)).strftime('%Y-%m-%d'))

        self.RfRate: pd.DataFrame = _RfRate.resample(
            '10Min').mean().fillna(method='ffill')
        df['RfRate'] = self.RfRate
        self.df: pd.DataFrame = df.fillna(method='ffill')

        if atOptionTradeOnly:
            pd.options.mode.chained_assignment = None  # default='warn'
            ATunderlyingMrktPrice['RfRate'] = [
                pd.NA for i in ATunderlyingMrktPrice.index]
            for i in self.RfRate.index:
                _i: str = str(i)[:10]
                for n, m in enumerate(ATunderlyingMrktPrice.index):
                    if _i in str(m):
                        if len(self.RfRate.loc[i].values) == 2:
                            ATunderlyingMrktPrice[
                                'RfRate'].iloc[n] = self.RfRate.loc[i].values[0][0]
                        elif len(self.RfRate.loc[i].values) == 1:
                            ATunderlyingMrktPrice[
                                'RfRate'].iloc[n] = self.RfRate.loc[i].values[0]
            self.ATdf: pd.DataFrame = ATunderlyingMrktPrice.copy().fillna(method='ffill')  # This is in case there were no Risk Free datapoints released after a certain time, but trades on the option still went through.

        if timeZoneInGraph != 'GMT':
            if atOptionTradeOnly:
                self.ATdf.index = [
                    self.ATdf.index[i].replace(
                        tzinfo=pytz.timezone(
                            'GMT')).astimezone(
                        tz=datetime.now().astimezone().tzinfo)
                    for i in range(len(self.ATdf))]
            else:
                df.index = [
                    df.index[i].replace(
                        tzinfo=pytz.timezone(
                            'GMT')).astimezone(
                        tz=timeZoneInGraph.tzinfo)
                    for i in range(len(df))]

        # Define our message to the calculation endpoint in the RDP (Refinitiv Data Platform) API, `atOptionTradeOnly`:
        self.universeL: List[rdf._base_definition.BaseDefinition]
        if atOptionTradeOnly:
            self.universeL = [
                option.Definition(
                    underlying_type=option.UnderlyingType.ETI,
                    buy_sell='Buy',
                    instrument_code=self.instrument,
                    strike=float(self.strikePrice),
                    pricing_parameters=option.PricingParameters(
                        market_value_in_deal_ccy=float(
                            self.ATdf[
                                f"{optnMrktPriceField}_{self.instrument}_OptPr"][i]),
                        risk_free_rate_percent=float(self.ATdf['RfRate'][i]),
                        underlying_price=float(
                            self.ATdf[
                                f"{self.underMrktPriceField}_{self.underlying}_underlying"][i]),
                        pricing_model_type='BlackScholes',
                        volatility_type='Implied',
                        underlying_time_stamp='Default',
                        report_ccy='EUR'))
                for i in range(len(self.ATdf.index))]
        else:
            self.universeL = [
                option.Definition(
                    underlying_type=option.UnderlyingType.ETI,
                    buy_sell='Buy',
                    instrument_code=self.instrument,
                    strike=float(self.strikePrice),
                    pricing_parameters=option.PricingParameters(
                        market_value_in_deal_ccy=float(df[optnMrktPriceField][i]),
                        risk_free_rate_percent=float(df.RfRate[i]),
                        underlying_price=float(
                            df[f"underlying {self.underlying} {self.underMrktPriceField}"][i]),
                        pricing_model_type='BlackScholes',
                        volatility_type='Implied',
                        underlying_time_stamp='Default',
                        report_ccy='EUR'))
                for i in range(len(df.index))]

        # We would like to keep a minimum of these fields in the Search Responce in order to construct following graphs:
        for k in ["MarketValueInDealCcy", "RiskFreeRatePercent",
                  "UnderlyingPrice", "PricingModelType", "DividendType",
                  "VolatilityType", "UnderlyingTimeStamp", "ReportCcy",
                  "VolatilityType", "Volatility"][::-1]:
            requestFields.insert(0, k)
        self.requestFields: List[str] = requestFields

        for i, p in enumerate(
            [self.universeL[i:i+searchBatchMax]
             for i in range(0, len(self.universeL), searchBatchMax)]):  # This list chunks our `universeL` in batches of `searchBatchMax`

            _IPADf: pd.DataFrame = rdf.Definitions(
                universe=p, fields=requestFields).get_data().data.df
            if i == 0:
                self.IPADf: pd.DataFrame = _IPADf
            else:
                self.IPADf: pd.DataFrame = self.IPADf.append(
                    _IPADf, ignore_index=True)

        if atOptionTradeOnly:
            self.IPADf.index = self.ATdf.index
        else:
            self.IPADf.index = self.df.index

        self.atOptionTradeOnly: bool = atOptionTradeOnly

        return self

    def Simple_graph(
        self,
        maxColwidth: int = 200,
        size: Tuple[int, int] = (15, 5),
        lineStyle: str = '.-',  # 'o-'
        plotting: str = 'Volatility',
        displayIndexInfo: bool = False
    ) -> IndexImpVolatNGreeksIPACalc:

        # We are going to want to show details about data retreived in a dataframe in the output of this function. The one line below allows us to maximise the width (column) length of cells to see all that is written within them.
        if displayIndexInfo:
            pd.options.display.max_colwidth = maxColwidth
            display(self.instrumentInfo)
        IPADfSimpleGraph: pd.DataFrame = pd.DataFrame(
            data=self.IPADf[[plotting]].values,
            index=self.IPADf[[plotting]].index)
        fig, axes = plt.subplots(ncols=1, figsize=size)
        axes.plot(IPADfSimpleGraph, lineStyle)
        if self.atOptionTradeOnly:
            axes.set_title(f"{self.instrument} {plotting} At Trade Only")
        else:
            axes.set_title(f"{self.instrument} {plotting}")

        self.plt = plt

        return self

    def Graph(
        self,
        include: Union[None, List[str]] = None,
        graphTemplate: str = 'plotly_dark',
        debug: bool=False
    ) -> IndexImpVolatNGreeksIPACalc:

        if include is None:
            include = self.requestFields

        try:  # This is needed in case there is not enough data to calculate values for all timestamps , see https://stackoverflow.com/questions/67244912/wide-format-csv-with-plotly-express
            self.IPADfGraph = self.IPADf[include]
            if debug: display(self.IPADfGraph)
            self.fig = px.line(self.IPADfGraph)
        except:
            try:
                print("Not all fields could be graphed")
                self.IPADfGraph = self.IPADfGraph[
                    ["ImpliedVolatility", "MarketValueInDealCcy",
                     "RiskFreeRatePercent", "UnderlyingPrice"]]
                self.fig = px.line(self.IPADfGraph)
            except:
                print("Not all fields could be graphed")
                self.IPADfGraph = self.IPADfGraph[
                    ["Volatility", "MarketValueInDealCcy",
                     "RiskFreeRatePercent", "UnderlyingPrice"]]
                self.fig = px.line(self.IPADfGraph)

        self.graphTemplate = graphTemplate

        return self

    def Overlay(
        self
    ) -> IndexImpVolatNGreeksIPACalc:

        self.fig.update_layout(
            title=self.instrument,
            template=self.graphTemplate)
        self.fig.for_each_trace(
            lambda t: t.update(
                visible=True if t.name in self.IPADfGraph.columns[:1] else "legendonly"))

        return self

    def Stack3(
        self,
        autosize: bool = False,
        width: int = 1300,
        height: int = 500
    ) -> IndexImpVolatNGreeksIPACalc:

        self.fig = plotly.subplots.make_subplots(rows=3, cols=1)

        self.fig.add_trace(go.Scatter(
            x=self.IPADf.index, y=self.IPADfGraph.ImpliedVolatility,
            name='Op Imp Volatility'), row=1, col=1)
        self.fig.add_trace(go.Scatter(
            x=self.IPADf.index, y=self.IPADfGraph.MarketValueInDealCcy,
            name='Op Mk Pr'), row=2, col=1)
        self.fig.add_trace(go.Scatter(
            x=self.IPADf.index, y=self.IPADfGraph.UnderlyingPrice,
            name=self.underlying+' Undrlyg Pr'), row=3, col=1)

        self.fig.update(layout_xaxis_rangeslider_visible=False)
        self.fig.update_layout(title=self.IPADfGraph.columns.name)
        self.fig.update_layout(
            title=self.instrument,
            template=self.graphTemplate,
            autosize=autosize,
            width=width,
            height=height)

        return self

<cell>389: error: Attribute "IPADf" already defined on line 387  [no-redef]


from functools import wraps
from time import time


# IndexImpVolatNGreeksIPACalc().Search_index_opt_ATM().IPA_calc().Graph(debug=True).Overlay().fig.show()
s = time()
t1 = IndexImpVolatNGreeksIPACalc()
print(time() - s)

0.0


s = time()
t2 = t1.Search_index_opt_ATM()
print(time() - s)

5.280257225036621


s = time()
t3 = t2.IPA_calc()
print(time() - s)

9.758203268051147


s = time()
t3 = t2.Graph()
print(time() - s)

Not all fields could be graphed
Not all fields could be graphed
0.07208371162414551

An error occurred while requesting URL('https://api.refinitiv.com/auth/oauth2/v1/token').
	ConnectError('[Errno 11001] getaddrinfo failed')
[Error 0 - None] [Errno 11001] getaddrinfo failed


s = time()
t4 = t3.Overlay().fig.show()
print(time() - s)

0.021039724349975586


IndexImpVolatNGreeksIPACalc().Search_index_opt_ATM().IPA_calc().Simple_graph().plt.show()


IndexImpVolatNGreeksIPACalc().Search_index_opt_ATM().IPA_calc().Graph().Stack3().fig.show()

Not all fields could be graphed
Not all fields could be graphed

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[111], line 441, in IndexImpVolatNGreeksIPACalc.Graph(self, include, graphTemplate, debug)
    440     if debug: display(self.IPADfGraph)
--> 441     self.fig = px.line(self.IPADfGraph)
    442 except:

File C:\JupyterLab\resources\jlab_server\lib\site-packages\plotly\express\_chart_types.py:264, in line(data_frame, x, y, line_group, color, line_dash, symbol, hover_name, hover_data, custom_data, text, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, error_x, error_x_minus, error_y, error_y_minus, animation_frame, animation_group, category_orders, labels, orientation, color_discrete_sequence, color_discrete_map, line_dash_sequence, line_dash_map, symbol_sequence, symbol_map, markers, log_x, log_y, range_x, range_y, line_shape, render_mode, title, template, width, height)
    260 """
    261 In a 2D line plot, each row of `data_frame` is represented as vertex of
    262 a polyline mark in 2D space.
    263 """
--> 264 return make_figure(args=locals(), constructor=go.Scatter)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\plotly\express\_core.py:1990, in make_figure(args, constructor, trace_patch, layout_patch)
   1988 apply_default_cascade(args)
-> 1990 args = build_dataframe(args, constructor)
   1991 if constructor in [go.Treemap, go.Sunburst, go.Icicle] and args["path"] is not None:

File C:\JupyterLab\resources\jlab_server\lib\site-packages\plotly\express\_core.py:1405, in build_dataframe(args, constructor)
   1403 # now that things have been prepped, we do the systematic rewriting of `args`
-> 1405 df_output, wide_id_vars = process_args_into_dataframe(
   1406     args, wide_mode, var_name, value_name
   1407 )
   1409 # now that `df_output` exists and `args` contains only references, we complete
   1410 # the special-case and wide-mode handling by further rewriting args and/or mutating
   1411 # df_output

File C:\JupyterLab\resources\jlab_server\lib\site-packages\plotly\express\_core.py:1222, in process_args_into_dataframe(args, wide_mode, var_name, value_name)
   1221         col_name = str(argument)
-> 1222         df_output[col_name] = to_unindexed_series(df_input[argument])
   1223 # ----------------- argument is likely a column / array / list.... -------
   1224 else:

File C:\JupyterLab\resources\jlab_server\lib\site-packages\plotly\express\_core.py:1073, in to_unindexed_series(x)
   1067 """
   1068 assuming x is list-like or even an existing pd.Series, return a new pd.Series with
   1069 no index, without extracting the data from an existing Series via numpy, which
   1070 seems to mangle datetime columns. Stripping the index from existing pd.Series is
   1071 required to get things to match up right in the new DataFrame we're building
   1072 """
-> 1073 return pd.Series(x).reset_index(drop=True)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\pandas\core\series.py:355, in Series.__init__(self, data, index, dtype, name, copy, fastpath)
    353 name = ibase.maybe_extract_name(name, data, type(self))
--> 355 if is_empty_data(data) and dtype is None:
    356     # gh-17261
    357     warnings.warn(
    358         "The default dtype for empty Series will be 'object' instead "
    359         "of 'float64' in a future version. Specify a dtype explicitly "
   (...)
    362         stacklevel=2,
    363     )

File C:\JupyterLab\resources\jlab_server\lib\site-packages\pandas\core\construction.py:796, in is_empty_data(data)
    795 is_list_like_without_dtype = is_list_like(data) and not hasattr(data, "dtype")
--> 796 is_simple_empty = is_list_like_without_dtype and not data
    797 return is_none or is_simple_empty

File C:\JupyterLab\resources\jlab_server\lib\site-packages\pandas\core\generic.py:1537, in NDFrame.__nonzero__(self)
   1535 @final
   1536 def __nonzero__(self):
-> 1537     raise ValueError(
   1538         f"The truth value of a {type(self).__name__} is ambiguous. "
   1539         "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
   1540     )

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
Cell In[111], line 445, in IndexImpVolatNGreeksIPACalc.Graph(self, include, graphTemplate, debug)
    444 print("Not all fields could be graphed")
--> 445 self.IPADfGraph = self.IPADfGraph[
    446     ["ImpliedVolatility", "MarketValueInDealCcy",
    447      "RiskFreeRatePercent", "UnderlyingPrice"]]
    448 self.fig = px.line(self.IPADfGraph)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\pandas\core\frame.py:3464, in DataFrame.__getitem__(self, key)
   3463         key = list(key)
-> 3464     indexer = self.loc._get_listlike_indexer(key, axis=1)[1]
   3466 # take() does not accept boolean indexers

File C:\JupyterLab\resources\jlab_server\lib\site-packages\pandas\core\indexing.py:1314, in _LocIndexer._get_listlike_indexer(self, key, axis)
   1312     keyarr, indexer, new_indexer = ax._reindex_non_unique(keyarr)
-> 1314 self._validate_read_indexer(keyarr, indexer, axis)
   1316 if needs_i8_conversion(ax.dtype) or isinstance(
   1317     ax, (IntervalIndex, CategoricalIndex)
   1318 ):
   1319     # For CategoricalIndex take instead of reindex to preserve dtype.
   1320     #  For IntervalIndex this is to map integers to the Intervals they match to.

File C:\JupyterLab\resources\jlab_server\lib\site-packages\pandas\core\indexing.py:1377, in _LocIndexer._validate_read_indexer(self, key, indexer, axis)
   1376 not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique())
-> 1377 raise KeyError(f"{not_found} not in index")

KeyError: "['ImpliedVolatility'] not in index"

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
Cell In[114], line 1
----> 1 IndexImpVolatNGreeksIPACalc().Search_index_opt_ATM().IPA_calc().Graph().Stack3().fig.show()

Cell In[111], line 454, in IndexImpVolatNGreeksIPACalc.Graph(self, include, graphTemplate, debug)
    450         print("Not all fields could be graphed")
    451         self.IPADfGraph = self.IPADfGraph[
    452             ["Volatility", "MarketValueInDealCcy",
    453              "RiskFreeRatePercent", "UnderlyingPrice"]]
--> 454         self.fig = px.line(self.IPADfGraph)
    456 self.graphTemplate = graphTemplate
    458 return self

File C:\JupyterLab\resources\jlab_server\lib\site-packages\plotly\express\_chart_types.py:264, in line(data_frame, x, y, line_group, color, line_dash, symbol, hover_name, hover_data, custom_data, text, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, error_x, error_x_minus, error_y, error_y_minus, animation_frame, animation_group, category_orders, labels, orientation, color_discrete_sequence, color_discrete_map, line_dash_sequence, line_dash_map, symbol_sequence, symbol_map, markers, log_x, log_y, range_x, range_y, line_shape, render_mode, title, template, width, height)
    216 def line(
    217     data_frame=None,
    218     x=None,
   (...)
    258     height=None,
    259 ) -> go.Figure:
    260     """
    261     In a 2D line plot, each row of `data_frame` is represented as vertex of
    262     a polyline mark in 2D space.
    263     """
--> 264     return make_figure(args=locals(), constructor=go.Scatter)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\plotly\express\_core.py:1990, in make_figure(args, constructor, trace_patch, layout_patch)
   1987 layout_patch = layout_patch or {}
   1988 apply_default_cascade(args)
-> 1990 args = build_dataframe(args, constructor)
   1991 if constructor in [go.Treemap, go.Sunburst, go.Icicle] and args["path"] is not None:
   1992     args = process_dataframe_hierarchy(args)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\plotly\express\_core.py:1405, in build_dataframe(args, constructor)
   1402     args["color"] = None
   1403 # now that things have been prepped, we do the systematic rewriting of `args`
-> 1405 df_output, wide_id_vars = process_args_into_dataframe(
   1406     args, wide_mode, var_name, value_name
   1407 )
   1409 # now that `df_output` exists and `args` contains only references, we complete
   1410 # the special-case and wide-mode handling by further rewriting args and/or mutating
   1411 # df_output
   1413 count_name = _escape_col_name(df_output, "count", [var_name, value_name])

File C:\JupyterLab\resources\jlab_server\lib\site-packages\plotly\express\_core.py:1222, in process_args_into_dataframe(args, wide_mode, var_name, value_name)
   1220     else:
   1221         col_name = str(argument)
-> 1222         df_output[col_name] = to_unindexed_series(df_input[argument])
   1223 # ----------------- argument is likely a column / array / list.... -------
   1224 else:
   1225     if df_provided and hasattr(argument, "name"):

File C:\JupyterLab\resources\jlab_server\lib\site-packages\plotly\express\_core.py:1073, in to_unindexed_series(x)
   1066 def to_unindexed_series(x):
   1067     """
   1068     assuming x is list-like or even an existing pd.Series, return a new pd.Series with
   1069     no index, without extracting the data from an existing Series via numpy, which
   1070     seems to mangle datetime columns. Stripping the index from existing pd.Series is
   1071     required to get things to match up right in the new DataFrame we're building
   1072     """
-> 1073     return pd.Series(x).reset_index(drop=True)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\pandas\core\series.py:355, in Series.__init__(self, data, index, dtype, name, copy, fastpath)
    351 else:
    353     name = ibase.maybe_extract_name(name, data, type(self))
--> 355     if is_empty_data(data) and dtype is None:
    356         # gh-17261
    357         warnings.warn(
    358             "The default dtype for empty Series will be 'object' instead "
    359             "of 'float64' in a future version. Specify a dtype explicitly "
   (...)
    362             stacklevel=2,
    363         )
    364         # uncomment the line below when removing the DeprecationWarning
    365         # dtype = np.dtype(object)

File C:\JupyterLab\resources\jlab_server\lib\site-packages\pandas\core\construction.py:796, in is_empty_data(data)
    794 is_none = data is None
    795 is_list_like_without_dtype = is_list_like(data) and not hasattr(data, "dtype")
--> 796 is_simple_empty = is_list_like_without_dtype and not data
    797 return is_none or is_simple_empty

File C:\JupyterLab\resources\jlab_server\lib\site-packages\pandas\core\generic.py:1537, in NDFrame.__nonzero__(self)
   1535 @final
   1536 def __nonzero__(self):
-> 1537     raise ValueError(
   1538         f"The truth value of a {type(self).__name__} is ambiguous. "
   1539         "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
   1540     )

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().


test1 = IndexImpVolatNGreeksIPACalc(indexUnderlying=".SPX")
test2 = test1.Search_index_opt_ATM(
    debug=False,
    after=15,
    callOrPut='Put',
    searchFields=["ExchangeCode", "UnderlyingQuoteName"],
    includeWeeklyOpts=False,
    topNuSearchResults=10,
    timeOfCalcDatetime=datetime.now(),
    underMrktPriceField="TRDPRC_1")
test3 = test2.IPA_calc(
    dateBack=3,
    optnMrktPriceField="TRDPRC_1",
    debug=False,
    atOptionTradeOnly=True,
    riskFreeRate=None,
    riskFreeRateField=None,
    timeZoneInGraph=datetime.now().astimezone())


test3.IPADf


test4 = test3.Simple_graph(
    maxColwidth=200,
    size=(15, 5),
    lineStyle='.-',  # 'o-'
    plotting='Volatility'
)


test4.plt.show()


# rd.close_session()  # It's good practice to close our RD session when done.


# # !pip3 install --trusted-host pypi.org dash_tvlwc
# # !pip3 install --trusted-host pypi.org dash

# import random

# import dash_tvlwc
# import dash
# from dash.dependencies import Input, Output, State
# from dash import html

# from datetime import datetime
# import random

# import pandas as pd

# def generate_random_series(*args, **kwargs):
#     return generate_random_ohlc(*args, **kwargs, close_only=True)

# def generate_random_ohlc(v0: float, ret=0.05, n=500, t0='2021-01-01', close_only=False):
#     datelist = [dt.strftime('%Y-%m-%d') for dt in pd.date_range(t0, periods=n).tolist()]
#     res = []
#     c = v0
#     for dt in datelist:
#         o = c
#         c = o * (1 + random.uniform(-ret, ret))
#         if not close_only:
#             h = max(o, c) * (1 + random.uniform(0, ret))
#             l = min(o, c) * (1 + random.uniform(-ret, 0))
#             res.append({
#                 "time": dt,
#                 "open": o,
#                 "high": h,
#                 "low": l,
#                 "close": c
#             })
#         else:
#             res.append({
#                 "time": dt,
#                 "value": c
#             })
#         o = c
#     return res

# app = dash.Dash(__name__, external_stylesheets=['./assets/stylesheet.css'])

# chart_options = {
#     'layout': {
#         'background': {'type': 'solid', 'color': '#1B2631'},
#         'textColor': 'white',
#     },
#     'grid': {
#         'vertLines': {'visible': False},
#         'horzLines': {'visible': False},
#     },
#     'localization': {'locale': 'en-US'}
# }

# panel1 = [
#     html.H2('Bar'),
#     dash_tvlwc.Tvlwc(
#         id='bar-chart',
#         seriesData=[generate_random_ohlc(v0=100, n=50)],
#         seriesTypes=['bar'],
#         width='100%',
#         chartOptions=chart_options
#     )
# ]


# p2_series = generate_random_ohlc(v0=1, n=50, ret=0.1)
# p2_series = [{'time': v['time']} if 12 < idx < 20 or idx > 45 else v for idx, v in enumerate(p2_series)]
# panel2 = [
#     html.H2('Candlestick'),
#     dash_tvlwc.Tvlwc(
#         id='candlestick-chart',
#         seriesData=[p2_series],
#         seriesTypes=['candlestick'],
#         seriesOptions=[{
#             'downColor': '#a6269a',
#             'upColor': '#ffaa30',
#             'borderColor': 'black',
#             'wickColor': 'black'
#         }],
#         width='100%',
#         chartOptions={'layout': {'background': {'type': 'solid', 'color': 'white'}}}
#     )
# ]


# panel3 = [
#     html.H2('Area'),
#     dash_tvlwc.Tvlwc(
#         id='area-chart',
#         seriesData=[generate_random_series(v0=15, n=50)],
#         seriesTypes=['area'],
#         seriesOptions=[{
#             'lineColor': '#FFAA30',
#             'topColor': '#2962FF',
#             'bottomColor': 'rgba(180, 98, 200, 0.1)',
#             'priceLineWidth': 3,
#             'priceLineColor': 'red'
#         }],
#         width='100%',
#         chartOptions=chart_options
#     )
# ]


# p4_series = generate_random_series(v0=5000, n=50)
# p4_mean = sum([p['value'] for p in p4_series]) / 50
# p4_max = max([p['value'] for p in p4_series])
# price_lines = [{'price': p4_max, 'color': '#2962FF', 'lineStyle': 0, 'title': 'MAX PRICE', 'axisLabelVisible': True}]
# panel4 = [
#     html.H2('Baseline'),
#     dash_tvlwc.Tvlwc(
#         id='baseline-chart',
#         seriesData=[p4_series],
#         seriesTypes=['baseline'],
#         seriesOptions=[{
#             'baseValue': {'type': 'price', 'price': p4_mean},
#             'topFillColor1': 'black',
#             'topFillColor2': 'rgba(255,255,255,0)',
#             'topLineColor': 'black',
#             'crosshairMarkerRadius': 8,
#             'lineWidth': 5,
#             'priceScaleId': 'left'
#         }],
#         seriesPriceLines=[price_lines],
#         width='100%',
#         chartOptions={
#             'rightPriceScale': {'visible': False},
#             'leftPriceScale': {'visible': True, 'borderColor': 'rgba(197, 203, 206, 1)',},
#             'timeScale': {'visible': False},
#             'grid': {'vertLines': {'visible': False}, 'horzLines': {'style': 0, 'color': 'black'}},
#         }
#     )
# ]


# # add markers and add color to volume bar
# p5_series = generate_random_series(v0=1, n=50, ret=0.1)
# markers = [
#     {'time': p5_series[15]['time'], 'position': 'aboveBar', 'color': '#f68410', 'shape': 'circle', 'text': 'Signal'},
#     {'time': p5_series[20]['time'], 'position': 'belowBar', 'color': 'white', 'shape': 'arrowUp', 'text': 'Buy'}
# ]
# p5_series_volume = generate_random_series(v0=100, n=50, ret=0.05)
# for i in p5_series_volume:
#     i['color'] = random.choice(['rgba(0, 150, 136, 0.8)', 'rgba(255,82,82, 0.8)'])

# panel5 = [
#     html.H2('Line and volume'),
#     dash_tvlwc.Tvlwc(
#         id='line-chart',
#         seriesData=[p5_series, p5_series_volume],
#         seriesTypes=['line', 'histogram'],
#         seriesOptions=[
#             {
#                 'lineWidth': 1
#             },
#             {
#                 'color': '#26a69a',
#                 'priceFormat': {'type': 'volume'},
#                 'priceScaleId': '',
#                 'scaleMargins': {'top': 0.9, 'bottom': 0},
#                 'priceLineVisible': False
#             },
#         ],
#         seriesMarkers=[markers],
#         width='100%',
#         chartOptions=chart_options
#     )
# ]


# p6_series = generate_random_series(v0=100, n=50, ret=0.3)
# for idx, _ in enumerate(p6_series):
#     if idx in [5,12,13,14,20,33,34,46]:
#         p6_series[idx]['color'] = 'white'

# panel6 = [
#     html.H2('Histogram'),
#     dash_tvlwc.Tvlwc(
#         id='histogram-chart',
#         seriesData=[p6_series],
#         seriesTypes=['histogram'],
#         seriesOptions=[{
#             'color': '#ff80cc',
#             'base': 100,
#             'priceLineVisible': False,
#             'lastValueVisible': False
#         }],
#         width='100%',
#         chartOptions={'layout': {'textColor': '#ff80cc', 'background': {'type': 'solid', 'color': 'black'}}}
#     )
# ]

# app.layout = html.Div([
#     html.H1('Chart options and series options'),
#     html.Div(className='container', children=[
#         html.Div(className='one', children=panel1),
#         html.Div(className='two', children=panel2),
#         html.Div(className='three', children=panel3),
#         html.Div(className='four', children=panel4),
#         html.Div(className='five', children=panel5),
#         html.Div(className='six', children=panel6),
#     ])
# ])

# if __name__ == '__main__':
#     app.run_server(debug=True)


# !where python
# import os, signal
# os.kill(os.getpid(), signal.SIGTERM)

[Error 400 - invalid_grant] empty error description

	DocumentTitle	RIC	StrikePrice	ExchangeCode	ExpiryDate	UnderlyingQuoteRIC
0	Eurex Dow Jones EURO STOXX 50 Index Option 430...	STXE43000C3.EX	4300	EUX	2023-03-17	[.STOXX50E]
1	Eurex Dow Jones EURO STOXX 50 Index Option 425...	STXE42500C3.EX	4250	EUX	2023-03-17	[.STOXX50E]
2	Eurex Dow Jones EURO STOXX 50 Index Option 420...	STXE42000C3.EX	4200	EUX	2023-03-17	[.STOXX50E]
3	Eurex Dow Jones EURO STOXX 50 Index Option 422...	STXE42250C3.EX	4225	EUX	2023-03-17	[.STOXX50E]
4	Eurex Dow Jones EURO STOXX 50 Index Option 427...	STXE42750C3.EX	4275	EUX	2023-03-17	[.STOXX50E]
...	...	...	...	...	...	...
95	Eurex Dow Jones EURO STOXX 50 Index Option 362...	STXE36250C3.EX	3625	EUX	2023-03-17	[.STOXX50E]
96	Eurex Dow Jones EURO STOXX 50 Index Option 405...	STXE40500E3.EX	4050	EUX	2023-05-19	[.STOXX50E]
97	Eurex Dow Jones EURO STOXX 50 Index Option 447...	STXE44750D3.EX	4475	EUX	2023-04-21	[.STOXX50E]
98	Eurex Dow Jones EURO STOXX 50 Index Option 417...	STXE41750E3.EX	4175	EUX	2023-05-19	[.STOXX50E]
99	Eurex Dow Jones EURO STOXX 50 Index Option 405...	STXE40500F3.EX	4050	EUX	2023-06-16	[.STOXX50E]

	DocumentTitle	RIC	StrikePrice	ExchangeCode	ExpiryDate	UnderlyingQuoteRIC
0	Eurex Dow Jones EURO STOXX 50 Index Option 425...	STXE42500D3.EX	4250	EUX	2023-04-21	[.STOXX50E]
1	Eurex Dow Jones EURO STOXX 50 Index Option 420...	STXE42000D3.EX	4200	EUX	2023-04-21	[.STOXX50E]
2	Eurex Dow Jones EURO STOXX 50 Index Option 430...	STXE43000D3.EX	4300	EUX	2023-04-21	[.STOXX50E]
3	Eurex Dow Jones EURO STOXX 50 Index Option 410...	STXE41000D3.EX	4100	EUX	2023-04-21	[.STOXX50E]
4	Eurex Dow Jones EURO STOXX 50 Index Option 415...	STXE41500D3.EX	4150	EUX	2023-04-21	[.STOXX50E]
...	...	...	...	...	...	...
150	Eurex Dow Jones EURO STOXX 50 Index Option 570...	STXE57000D3.EX	5700	EUX	2023-04-21	[.STOXX50E]
151	Eurex Dow Jones EURO STOXX 50 Index Option 100...	STXE100000D3.EX	10000	EUX	2023-04-21	[.STOXX50E]
152	Eurex Dow Jones EURO STOXX 50 Index Option 900...	STXE90000D3.EX	9000	EUX	2023-04-21	[.STOXX50E]
153	Eurex Dow Jones EURO STOXX 50 Index Option 527...	STXE52750D3.EX	5275	EUX	2023-04-21	[.STOXX50E]
154	Eurex Dow Jones EURO STOXX 50 Index Option 532...	STXE53250D3.EX	5325	EUX	2023-04-21	[.STOXX50E]

STXE42750D3.EX	IMP_VOLT
Timestamp
2022-12-07 00:54:54.453	15.4658
2022-12-08 00:54:40.323	15.6759
2022-12-10 00:54:49.609	15.3218
2022-12-22 00:53:27.211	15.0791
2022-12-23 00:53:27.595	15.0012

STXE42750D3.EX	TRDPRC_1
Timestamp
2023-03-02 08:20:00	63.5
2023-03-02 13:00:00	75.6
2023-03-02 13:10:00	75.0
2023-03-02 13:50:00	72.5
2023-03-02 14:40:00	79.2

STXE42750D3.EX	TRDPRC_1
Timestamp
2023-03-02 08:20:00	63.5
2023-03-02 08:30:00	63.5
2023-03-02 08:40:00	63.5
2023-03-02 08:50:00	63.5
2023-03-02 09:00:00	63.5
...	...
2023-03-07 15:20:00	116.4
2023-03-07 15:30:00	116.4
2023-03-07 15:40:00	116.4
2023-03-07 15:50:00	116.4
2023-03-07 16:00:00	95.9

Implied Volatility and Greeks of Index Options¶

EUREX Call Options¶

Finding Live Options (using Search API)¶

Function for Expiration days¶

Function to find the next expiring Option outside the next x day window¶

Refinitiv-provided Daily Implied Volatility¶

Option Price¶

Underlying Asset Price¶

Strike Price¶

Risk-Free Interest Rate¶

Annualized Continuous Dividend Rate¶

Calculating IV¶

All Trades¶

Delivery Layer¶

Content Layer¶

At Option Trade Only¶

Delivery Layer¶

Content Layer¶

Overlay¶

Sack of 3 Graphs¶

Simple Graph¶

EUREX, or SPX Call or Put Options¶

Finding Expired Options¶

STOXX50E Usecase¶

General Usecase¶

Creating a class with PEP 3107 (a.k.a.: Type Hints)¶

dash_tvlwc¶

Conclusion¶

References¶

Q&A¶

.STOXX50E	TRDPRC_1
Timestamp
2023-03-01 12:00:00	4273.75
2023-03-01 12:10:00	4272.33
2023-03-01 12:20:00	4271.45
2023-03-01 12:30:00	4272.11
2023-03-01 12:40:00	4268.92
...	...
2023-03-08 10:10:00	4274.79
2023-03-08 10:20:00	4274.32
2023-03-08 10:30:00	4277.82
2023-03-08 10:40:00	4280.98
2023-03-08 10:50:00	4282.80

STXE42750D3.EX	underlying .STOXX50E TRDPRC_1	TRDPRC_1
Timestamp
2023-03-02 08:20:00	4184.42	63.5
2023-03-02 08:30:00	4179.43	63.5
2023-03-02 08:40:00	4194.54	63.5
2023-03-02 08:50:00	4203.87	63.5
2023-03-02 09:00:00	4204.93	63.5
...	...	...
2023-03-08 10:10:00	4274.79	95.9
2023-03-08 10:20:00	4274.32	95.9
2023-03-08 10:30:00	4277.82	95.9
2023-03-08 10:40:00	4280.98	95.9
2023-03-08 10:50:00	4282.80	95.9

EURIBOR3MD=	Fixing Value
Date
2023-02-28	2.744
2023-03-01	2.783
2023-03-02	2.801
2023-03-03	2.849
2023-03-06	2.875
2023-03-07	2.920
2023-03-08	2.944

STXE42750D3.EX	underlying .STOXX50E TRDPRC_1	TRDPRC_1	EurRfRate
2023-03-02 09:20:00+01:00	4184.42	63.5	2.801
2023-03-02 09:30:00+01:00	4179.43	63.5	2.801
2023-03-02 09:40:00+01:00	4194.54	63.5	2.801
2023-03-02 09:50:00+01:00	4203.87	63.5	2.801
2023-03-02 10:00:00+01:00	4204.93	63.5	2.801
...	...	...	...
2023-03-08 11:10:00+01:00	4274.79	95.9	2.920
2023-03-08 11:20:00+01:00	4274.32	95.9	2.920
2023-03-08 11:30:00+01:00	4277.82	95.9	2.920
2023-03-08 11:40:00+01:00	4280.98	95.9	2.920
2023-03-08 11:50:00+01:00	4282.80	95.9	2.920

STXE43000Q2.EX^E22	BID	ASK	TRDPRC_1	SETTLE
Date
2022-02-21	422.2	434.3	NaN	426.9
2022-02-22	420.1	433.1	NaN	424.7
2022-02-23	426.6	443.0	NaN	435.0
2022-02-24	552.2	573.1	NaN	563.0
2022-02-25	428.4	444.1	NaN	435.0
...	...	...	...	...
2022-05-16	611.5	643.0	628.8	627.0
2022-05-17	548.7	580.3	<NA>	565.0
2022-05-18	604.7	634.2	<NA>	618.8
2022-05-19	651.8	685.1	<NA>	669.7
2022-05-20	577.6	640.8	<NA>	600.2

STXE43000M1.EX^A21	BID	ASK	TRDPRC_1	SETTLE
Date
2020-10-23	1118.2	1123.3	<NA>	1122.7
2020-10-26	1193.9	1225.8	<NA>	1209.3
2020-10-27	1223.1	1255	<NA>	1238.3
2020-10-28	1344.4	1376.2	<NA>	1360.4
2020-10-29	1342.5	1373.8	<NA>	1357.2

SPXm212240000.U^A22	BID	ASK	TRDPRC_1
Date
2021-10-25	33.80	34.40	34.42
2021-10-26	34.70	35.30	34.95
2021-10-27	38.70	39.40	39.20
2021-10-28	34.00	34.60	33.54
2021-10-29	32.10	32.60	34.13
...	...	...	...
2022-01-13	0.55	0.65	0.57
2022-01-14	0.35	0.50	0.40
2022-01-18	0.35	0.45	0.40
2022-01-19	0.20	0.30	0.25
2022-01-20	0.05	0.15	0.15

	TRDPRC_1_OptPr	TRDPRC_1_UnderlyingPr
Timestamp
2023-03-02 08:20:00	63.5	4184.42
2023-03-02 13:00:00	75.6	4219.54
2023-03-02 13:10:00	75.0	4217.75
2023-03-02 13:50:00	72.5	4211.76
2023-03-02 14:40:00	79.2	4226.34
2023-03-02 14:50:00	81.4	4230.12
2023-03-02 15:00:00	82.7	4236.22
2023-03-02 15:20:00	82.7	4235.87
2023-03-02 15:50:00	83.2	4237.32
2023-03-03 08:10:00	94.6	4265.75
2023-03-03 10:00:00	99.5	4275.45
2023-03-03 11:30:00	103.1	4279.28
2023-03-03 11:50:00	102.7	4279.87
2023-03-03 12:00:00	104.4	4282.69
2023-03-03 12:30:00	104.4	4279.62
2023-03-03 14:10:00	105.0	4284.80
2023-03-03 15:50:00	106.8	4290.44
2023-03-03 16:00:00	107.3	4291.99
2023-03-03 16:10:00	110.7	4297.49
2023-03-06 08:40:00	122.2	4319.68
2023-03-06 09:30:00	118.0	4311.41
2023-03-06 13:50:00	118.0	4313.63
2023-03-06 16:20:00	116.4	4311.68
2023-03-07 16:00:00	95.9	4280.87

	MarketValueInDealCcy	RiskFreeRatePercent	UnderlyingPrice	PricingModelType	DividendType	VolatilityType	UnderlyingTimeStamp	ReportCcy	VolatilityType	Volatility	DeltaPercent	GammaPercent	RhoPercent	ThetaPercent	VegaPercent
0	63.5	2.801	4184.42	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	17.418367	0.374475	0.001495	1.812390	-1.097506	5.495620
1	63.5	2.801	4179.43	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	17.757155	0.369892	0.001462	1.787049	-1.112810	5.467574
2	63.5	2.801	4194.54	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	16.723446	0.384334	0.001565	1.866813	-1.065352	5.552583
3	63.5	2.801	4203.87	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	16.072575	0.394176	0.001637	1.921008	-1.034258	5.604909
4	63.5	2.801	4204.93	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	15.997958	0.395344	0.001645	1.927434	-1.030630	5.610824
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
223	95.9	2.920	4274.79	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	16.069137	0.513188	0.001667	2.528938	-1.097071	5.899853
224	95.9	2.920	4274.32	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	16.109989	0.512425	0.001663	2.524720	-1.099722	5.899610
225	95.9	2.920	4277.82	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	15.804338	0.518196	0.001693	2.556640	-1.079779	5.900826
226	95.9	2.920	4280.98	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	15.525421	0.523601	0.001720	2.586507	-1.061356	5.900667
227	95.9	2.920	4282.80	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	15.363433	0.526803	0.001737	2.604187	-1.050558	5.899982

STXE42750D3.EX	MarketValueInDealCcy	RiskFreeRatePercent	UnderlyingPrice	PricingModelType	DividendType	VolatilityType	UnderlyingTimeStamp	ReportCcy	VolatilityType	ImpliedVolatility	DeltaPercent	GammaPercent	RhoPercent	ThetaPercent	VegaPercent
2023-03-02 09:20:00+01:00	63.5	2.801	4184.42	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	17.418367	0.374475	0.001495	1.81239	-1.097506	5.49562
2023-03-02 09:30:00+01:00	63.5	2.801	4179.43	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	17.757155	0.369892	0.001462	1.787049	-1.11281	5.467574
2023-03-02 09:40:00+01:00	63.5	2.801	4194.54	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	16.723446	0.384334	0.001565	1.866813	-1.065352	5.552583
2023-03-02 09:50:00+01:00	63.5	2.801	4203.87	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	16.072575	0.394176	0.001637	1.921008	-1.034258	5.604909
2023-03-02 10:00:00+01:00	63.5	2.801	4204.93	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	15.997958	0.395344	0.001645	1.927434	-1.03063	5.610824
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-03-08 11:10:00+01:00	95.9	2.92	4274.79	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	16.069137	0.513188	0.001667	2.528938	-1.097071	5.899853
2023-03-08 11:20:00+01:00	95.9	2.92	4274.32	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	16.109989	0.512425	0.001663	2.52472	-1.099722	5.89961
2023-03-08 11:30:00+01:00	95.9	2.92	4277.82	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	15.804338	0.518196	0.001693	2.55664	-1.079779	5.900826
2023-03-08 11:40:00+01:00	95.9	2.92	4280.98	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	15.525421	0.523601	0.00172	2.586507	-1.061356	5.900667
2023-03-08 11:50:00+01:00	95.9	2.92	4282.8	BlackScholes	ImpliedYield	Calculated	Default	EUR	Calculated	15.363433	0.526803	0.001737	2.604187	-1.050558	5.899982

	TRDPRC_1_OptPr	TRDPRC_1_UnderlyingPr	EurRfRate
2023-03-02 09:20:00+01:00	63.5	4184.42	2.801
2023-03-02 14:00:00+01:00	75.6	4219.54	2.801
2023-03-02 14:10:00+01:00	75.0	4217.75	2.801
2023-03-02 14:50:00+01:00	72.5	4211.76	2.801
2023-03-02 15:40:00+01:00	79.2	4226.34	2.801
2023-03-02 15:50:00+01:00	81.4	4230.12	2.801
2023-03-02 16:00:00+01:00	82.7	4236.22	2.801
2023-03-02 16:20:00+01:00	82.7	4235.87	2.801
2023-03-02 16:50:00+01:00	83.2	4237.32	2.801
2023-03-03 09:10:00+01:00	94.6	4265.75	2.849
2023-03-03 11:00:00+01:00	99.5	4275.45	2.849
2023-03-03 12:30:00+01:00	103.1	4279.28	2.849
2023-03-03 12:50:00+01:00	102.7	4279.87	2.849
2023-03-03 13:00:00+01:00	104.4	4282.69	2.849
2023-03-03 13:30:00+01:00	104.4	4279.62	2.849
2023-03-03 15:10:00+01:00	105.0	4284.80	2.849
2023-03-03 16:50:00+01:00	106.8	4290.44	2.849
2023-03-03 17:00:00+01:00	107.3	4291.99	2.849
2023-03-03 17:10:00+01:00	110.7	4297.49	2.849
2023-03-06 09:40:00+01:00	122.2	4319.68	2.875
2023-03-06 10:30:00+01:00	118.0	4311.41	2.875
2023-03-06 14:50:00+01:00	118.0	4313.63	2.875
2023-03-06 17:20:00+01:00	116.4	4311.68	2.875
2023-03-07 17:00:00+01:00	95.9	4280.87	2.920